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Introduction: Brachytherapy is a widely used cancer treatment technique requiring precise dose delivery for optimal outcomes. This study evaluates the dosimetric properties of the ${{\rm{\;}}^{192}}$ Ir Flexisource using Gafchromic EBT3 film, adhering to American Association of Physicists in Medicine guidelines. Methods: The EBT3 film was calibrated with a linear accelerator to establish the relationship between optical density and absorbed dose. Measurements were conducted in a water-equivalent solid phantom to simulate human tissue. The dosimetric properties measured included the dose rate constant, radial dose function and anisotropy function. Results: The results showed a close alignment with reference data: a maximum deviation of 3·1% for the dose rate constant, 2·3% for the radial dose function and 5·67% for the anisotropy function. These deviations are within acceptable limits, indicating high accuracy. Conclusions: Gafchromic EBT3 film demonstrates reliable dosimetric measurement in high-dose rate brachytherapy. The film's accuracy in evaluating the ${{\rm{\;}}^{192}}$ Ir Flexisource ensures precise treatment delivery and enhances patient safety, supporting its continued use in clinical practice. [ABSTRACT FROM AUTHOR]

Objective Maternal risk stratification systems are increasingly employed in predicting and preventing obstetric complications. These systems focus primarily on maternal morbidity, and few tools exist to stratify neonatal risk. We sought to determine if a maternal risk stratification score was associated with neonatal morbidity. Study Design Retrospective cohort study of patients with liveborn infants born at ≥24 weeks at four hospitals in one health system between January 1, 2020, and December 31, 2020. The Expanded Obstetric Comorbidity Score (EOCS) is used as the maternal risk score. The primary neonatal outcome was 5-minute Apgar <7. Logistic regression models determined associations between EOCS and neonatal morbidity. Secondary analyses were performed, including stratifying outcomes by gestational age and limiting analysis to "low-risk" term singletons. Model discrimination assessed using the area under the receiver operating characteristic curves (AUC) and calibration via calibration plots. Results A total of 14,497 maternal–neonatal pairs were included; 236 (1.6%) had 5-minute Apgar <7; EOCS was higher in 5-minute Apgar <7 group (median 41 vs. 11, p < 0.001). AUC for EOCS in predicting Apgar <7 was 0.72 (95% Confidence Interval (CI) 0.68, 0.75), demonstrating relatively good discrimination. Calibration plot revealed that those in the highest EOCS decile had higher risk of neonatal morbidity (7.6 vs. 1.7%, p < 0.001). When stratified by gestational age, discrimination weakened with advancing gestational age: AUC 0.70 for <28 weeks, 0.63 for 28 to 31 weeks, 0.64 for 32 to 36 weeks, and 0.61 for ≥37 weeks. When limited to term low-risk singletons, EOCS had lower discrimination for predicting neonatal morbidity and was not well calibrated. Conclusion A maternal morbidity risk stratification system does not perform well in most patients giving birth, at low risk for neonatal complications. The findings suggest that the association between EOCS and 5-minute Apgar <7 likely reflects a relationship with prematurity. This study cautions against intentional or unintentional extrapolation of maternal morbidity risk for neonatal risk, especially for term deliveries. Key Points EOCS had moderate discrimination for Apgar <7. Predictive performance declined when limited to low-risk term singletons. Relationship between EOCS and Apgar <7 was likely driven by prematurity. [ABSTRACT FROM AUTHOR]

Background Widespread outbreaks of person-to-person transmitted hepatitis A virus (HAV), particularly among people who inject drugs (PWID), continue across the United States and globally. However, the herd immunity threshold and vaccination coverage required to prevent outbreaks are unknown. We used surveillance data and dynamic modeling to estimate herd immunity thresholds among PWID in 16 US states. Methods We used a previously published dynamic model of HAV transmission calibrated to surveillance data from outbreaks involving PWID in 16 states. Using state-level calibrated models, we estimated the basic reproduction number (R 0) and herd immunity threshold for PWID in each state. We performed a meta-analysis of herd immunity thresholds to determine the critical vaccination coverage required to prevent most HAV outbreaks among PWID. Results Estimates of R 0 for HAV infection ranged from 2.2 (95% confidence interval [CI], 1.9–2.5) for North Carolina to 5.0 (95% CI, 4.5–5.6) for West Virginia. Corresponding herd immunity thresholds ranged from 55% (95% CI, 47%–61%) for North Carolina to 80% (95% CI, 78%–82%) for West Virginia. Based on the meta-analysis, we estimated a pooled herd immunity threshold of 64% (95% CI, 61%–68%; 90% prediction interval, 52%–76%) among PWID. Using the prediction interval upper bound (76%) and assuming 95% vaccine efficacy, we estimated that vaccination coverage of 80% could prevent most HAV outbreaks. Conclusions Hepatitis A vaccination programs in the United States may need to achieve vaccination coverage of at least 80% among PWID in order to prevent most HAV outbreaks among this population. [ABSTRACT FROM AUTHOR]

Background: The outcome of clinical treatment for idiopathic sudden sensorineural hearing loss (ISSNHL) is frequently the primary concern. Aim: For the convenient prediction of reference prognosis outcomes in patients with ISSNHL. Materials and methods: Patients diagnosed with ISSNHL who were admitted to the otorhinolaryngology ward of Zhejiang Provincial Hospital of Traditional Chinese Medicine from January 2020 to December 2023 were included. Univariate and multivariate logistic regression analyses were employed to identify independent prognostic factors for the treatment outcome of ISSNHL, which were subsequently used to develop nomograms. Discrimination, calibration, and clinical utility were assessed to evaluate the performance of the ISSNHL nomograms. Results: 371 ISSNHL patients were enrolled in this study. Multivariable logistic regression analysis showed that history of deafness, aural fullness, time of onset, and age were independent prognostic factors for ISSNHL patients, with statistically significant regression coefficients. Predictive nomograms were developed with excellent discrimination, calibration, and clinical value. Conclusions: Leveraging data from ISSNHL patients, we developed a predictive nomogram to assess prognostic factors upon admission. This nomogram facilitates clinicians in approximating the likelihood of favorable prognosis. Significance: By accumulating clinical data from ISSNHL patients, it's anticipated that the possibility of recovery following treatment can be determined. [ABSTRACT FROM AUTHOR]

• Study surveyed vehicle owners who have had their crash avoidance features repaired. • Features of interest were front crash prevention, blind spot detection, and cameras. • Although repairs often improved feature performance, post-repair issues were common. • Calibration problems may have contributed to many of these post-repair issues. • The repair process needs to be simplified and repair solutions made more accessible. Introduction : Popularity of crash avoidance features is growing, but so too is confusion around how to repair them, how much repairs should cost, and who should pay for those repairs. This study's purpose was to capture how these issues are affecting consumers. Method : A total of 496 vehicle owners in the United States were surveyed online and by phone about their experiences repairing front crash prevention (n = 359), blind spot detection (n = 317), and/or driver assistance cameras (n = 348) equipped on their personal vehicles. Results : Owners tended to have multiple reasons for repairs. Repairs due to vehicle (i.e., crash or windshield) damage corresponded with the greatest likelihood of post-repair issues, especially if calibration was performed, and higher out-of-pocket costs (possibly because of deductibles or other repair work). About half of respondents who had calibrations performed on features repaired because of vehicle damage reported persisting issues with the features after repair. Post-repair issues were more common for repairs performed at independent repairers than dealership service centers, yet similar feature calibration rates were reported for both types of repairers. More people went to dealership service centers than independent repairers, and these respondents were more likely to say they would return to this type of facility for a similar repair in the future. Although most repairers explained why repairs occurred, less than half of respondents said they completely understood the reasons given. Conclusions : There are new complications in the repair cycle affecting consumers. Post-repair issues are more prevalent than previously assumed, regardless of the crash avoidance feature repaired. Practical applications : Post-repair issues risk undermining consumer acceptance and the safety potential of critical features. Simplifying the repair process and establishing affordable and accessible centralized databases with repair specifications and instructions from the manufacturers would be a start to addressing industry-wide challenges. [ABSTRACT FROM AUTHOR]

We analyse the spatial diffusion of new COVID-19 cases and the countrywide impact of state-specific containment policies during the early months of the COVID-19 pandemic in the United States. We first use spatial econometric techniques to document direct and indirect spillovers of new infections across county and state lines, as well as the impact of individual states' lockdown policies on infections in neighbouring states. We find consistent statistical evidence that new cases diffuse across county lines, holding county-level factors constant, and that the diffusion across counties was affected by the closure policies of adjacent states. We then develop a spatial version of the epidemiological susceptible–infected–recovered (SIR) model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighbouring states. We incorporate lockdown policies into our model and calibrate the model to match both the cumulative and the new infections across the 48 contiguous US states and DC. Our results suggest that had the states with the less restrictive social distancing measures tightened them by one level, the cumulative infections in other states would be about 5% smaller. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run. [ABSTRACT FROM AUTHOR]

Research in parental mediation often focuses on how parents' practices for managing digital media are aligned with normative expectations. However, there is less research that explores parental mediation as a process, with practices changing over time in response to barriers and challenges. To address this gap, the goal of the current study is to examine parents' decisions around not monitoring or limiting adolescents' media use. Based on focus group discussions and interviews with predominantly female (77%) and White (92%) parents living in five communities in the Midwestern United States, we explore parental mediation as a process in which decisions about children's media use reflect competing individual, ideological, and structural factors. In eight focus groups (n = 48) and 13 follow-up interviews, we ask parents to narrate barriers to commonly suggested mediation strategies to examine how parents' navigate factors such as efficacy, conflict, or adolescent autonomy in managing digital media use. Based on the findings, we propose that looking at barriers illustrates mediation as a process of calibration, a decision that is made and re-made as parents navigate complex and sometimes contradictory situations and expectations. Prior State of Knowledge: Past studies on parental mediation of digital technology define a range of strategies like restriction or monitoring and explore factors predicting whether parents use these strategies. Less research examines how parents arrive at the decision to implement normative mediation strategies. Novel Contributions: We propose the term calibration to explain how barriers and challenges prompt shifts in parental mediation over time. Calibration captures how parents balance competing internal and external factors, like efficacy or norms, in engaging with adolescents about digital media use. Practical Implications: Conceptualizing mediation as calibration may help parents develop a toolbox of strategies that can shift as needed. In addition, focusing on values like autonomy helps parents choose which mediation strategies work for their family context and which may not. [ABSTRACT FROM AUTHOR]

Background: Existing models to predict fall‐related injuries (FRI) in nursing homes (NH) focus on hip fractures, yet hip fractures comprise less than half of all FRIs. We developed and validated a series of models to predict the absolute risk of FRIs in NH residents. Methods: Retrospective cohort study of long‐stay US NH residents (≥100 days in the same facility) between January 1, 2016 and December 31, 2017 (n = 733,427) using Medicare claims and Minimum Data Set v3.0 clinical assessments. Predictors of FRIs were selected through LASSO logistic regression in a 2/3 random derivation sample and tested in a 1/3 validation sample. Sub‐distribution hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated for 6‐month and 2‐year follow‐up. Discrimination was evaluated via C‐statistic, and calibration compared the predicted rate of FRI to the observed rate. To develop a parsimonious clinical tool, we calculated a score using the five strongest predictors in the Fine‐Gray model. Model performance was repeated in the validation sample. Results: Mean (Q1, Q3) age was 85.0 (77.5, 90.6) years and 69.6% were women. Within 2 years of follow‐up, 43,976 (6.0%) residents experienced ≥1 FRI. Seventy predictors were included in the model. The discrimination of the 2‐year prediction model was good (C‐index = 0.70), and the calibration was excellent. Calibration and discrimination of the 6‐month model were similar (C‐index = 0.71). In the clinical tool to predict 2‐year risk, the five characteristics included independence in activities of daily living (ADLs) (HR 2.27; 95% CI 2.14–2.41) and a history of non‐hip fracture (HR 2.02; 95% CI 1.94–2.12). Performance results were similar in the validation sample. Conclusions: We developed and validated a series of risk prediction models that can identify NH residents at greatest risk for FRI. In NH, these models should help target preventive strategies. [ABSTRACT FROM AUTHOR]

BACKGROUND: The American College of Surgeons NSQIP risk calculator (RC) uses regression to make predictions for fourteen 30-day surgical outcomes. While this approach provides accurate (discrimination and calibration) risk estimates, they might be improved by machine learning (ML). To investigate this possibility, accuracy for regression-based risk estimates were compared to estimates from an extreme gradient boosting (XGB)-ML algorithm. STUDY DESIGN: A cohort of 5,020,713 million NSQIP patient records was randomly divided into 80% for model construction and 20% for validation. Risk predictions using regression and XGB-ML were made for 13 RC binary 30-day surgical complications and one continuous outcome (length of stay [LOS]). For the binary outcomes, discrimination was evaluated using the area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC), and calibration was evaluated using Hosmer--Lemeshow statistics. Mean squared error and a calibration curve analog were evaluated for the continuous LOS outcome. RESULTS: For every binary outcome, discrimination (AUROC and AUPRC) was slightly greater for XGB-ML than for regression (mean [across the outcomes] AUROC was 0.8299 vs 0.8251, and mean AUPRC was 0.1558 vs 0.1476, for XGB-ML and regression, respectively). For each outcome, miscalibration was greater (larger Hosmer--Lemeshow values) with regression; there was statistically significant miscalibration for all regression-based estimates, but only for 4 of 13 when XGB-ML was used. For LOS, mean squared error was lower for XGB-ML. CONCLUSIONS: XGB-ML provided more accurate risk estimates than regression in terms of discrimination and calibration. Differences in calibration between regression and XGB-ML were of substantial magnitude and support transitioning the RC to XGB-ML. [ABSTRACT FROM AUTHOR]

Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine‐learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K‐nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results: The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis‐related molar loss. In addition, the ensembled model showed the best calibration performance. Conclusions: Through a multi‐centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic, many students with hearing loss were required to learn through virtual instruction in their homes, while still receiving their public school accommodations, including remote microphone technology. The purpose of this project was to evaluate three virtual learning arrangements to determine acoustic characteristics of the transmitted signals. Two simulated testing arrangements were used: 1) An instructional inperson setup in one room: control (non-virtual) and 2) A remotelearning setup in two rooms (Virtual). In the virtual learning environment, three conditions were evaluated. The first condition was Hearing Aid in Soundfield (HA/SF) where the signal was received by the hearing aid worn by KEMAR via the speakers of the computer. Condition 2 was Bluetooth where the signal was received via a Bluetooth connection between the hearing aid and the computer. Condition 3 was Remote Mic where the signal was received via a digital modulation connection between the hearing aid and a Phonak Roger Touchscreen, which was hardwired to the computer. When comparing listening arrangements, the remote microphone and Teams platforms condition showed the least variability in relative output. There is a need for audiological intervention in virtual learning environments for students with hearing loss to ensure audibility is optimal. A protocol is suggested for verification of assistive technology interfaced with personal computers used by students with hearing loss in virtual learning environments. Audiologists need to be involved in the fitting and verification of assistive technology beyond dispensing the devices. If transparency is not achieved, and a student is in a challenging acoustic environment, educational success may be compromised. [ABSTRACT FROM AUTHOR]

Quantile treatment effects can be important causal estimands in the evaluation of biomedical treatments or interventions for health outcomes such as birthweight and medical cost. However, the existing estimators require either a propensity score model or a conditional density vector model is correctly specified, which is difficult to verify in practice. In this paper, we allow multiple models for propensity score and conditional density vector, then construct a class of calibration estimators based on multiple imputation and inverse probability weighting approaches via empirical likelihood. The resulting estimators multiply robust in the sense that they are consistent if any one of these models is correctly specified. Moreover, we propose another class of ensemble estimators to reduce computational burden while ensuring multiple robustness. Simulations are performed to evaluate the finite sample performance of the proposed estimators. Two applications to the birthweight of infants born in the United States and AIDS Clinical Trials Group Protocol 175 data are also presented. [ABSTRACT FROM AUTHOR]

Readmission to acute care from the inpatient rehabilitation facility (IRF) setting is potentially preventable and an important target of quality improvement and cost savings. The objective of this study was to develop a risk calculator to predict 30-day all-cause readmissions from the IRF setting. Retrospective database analysis using the Uniform Data System for Medical Rehabilitation (UDS MR) from 2015 through 2019. In total, 956 US inpatient rehabilitation facilities and 1,849,768 IRF discharges comprising patients from 14 impairment groups. Logistic regression models were developed to calculate risk-standardized 30-day all-cause hospital readmission rates for patients admitted to an IRF. Models for each impairment group were assessed using 12 common clinical and demographic variables and all but 4 models included various special variables. Models were assessed for discrimination (c-statistics), calibration (calibration plots), and internal validation (bootstrapping). A readmission risk scoring system was created for each impairment group population and was graphically validated. The mean age of the cohort was 68.7 (15.2) years, 50.7% were women, and 78.3% were Caucasian. Medicare was the primary payer for 73.1% of the study population. The final models for each impairment group included between 4 and 13 total predictor variables. Model c-statistics ranged from 0.65 to 0.70. There was good calibration represented for most models up to a readmission risk of 30%. Internal validation of the models using bootstrap samples revealed little bias. Point systems for determining risk of 30-day readmission were developed for each impairment group. Multivariable risk factor algorithms based upon administrative data were developed to assess 30-day readmission risk for patients admitted from IRF. This report represents the development of a readmission risk calculator for the IRF setting, which could be instrumental in identifying high risk populations for readmission and targeting resources towards a diverse group of IRF impairment groups. [ABSTRACT FROM AUTHOR]

With the advent of new sensing technologies and robust field-deployable analyzers, monitoring approaches can now generate valuable hazard information directly in the workplace. This is the case for monitoring respirable dust and respirable crystalline silica concentration levels. Estimating the quartz amount of a respirable dust sample by nondestructive analysis can be carried out using portable Fourier transform infrared spectroscopy (FTIR) units. Real-time respirable dust monitors, combined with small video cameras, allow advanced assessments using the Helmet-CAM methodology. These two field-based monitoring approaches, developed by the National Institute for Occupational Safety and Health (NIOSH), have been trialed in a sandstone quarry. Twenty-six Helmet-CAM sessions were conducted, and forty-one dust samples were collected around the quarry and analyzed on-site during two events. The generated data generated were used to characterize concentration levels for the monitored areas and workers, to identify good practices, and to illustrate activities that could be improved with additional engineered control technologies. Laboratory analysis of the collected samples complemented the field finding and provided an assessment of the performance of the field-based techniques. Only a fraction of the real-time respirable dust monitoring sessions data could be corrected with laboratory analysis. The average correction factor ratio was 5.0. Nevertheless, Helmet-CAM results provided valuable information for each session. The field-based quartz monitoring approach overestimated the concentration by a factor of 1.8, but it successfully assessed the quartz concentration trends in the quarry. The data collected could be used for the determination of a quarry calibration factor for future events. The quartz content in the dust was found to vary from 14% to 100%, and this indicates the need for multiple techniques in the characterization of respirable dust and quartz concentration and exposure. Overall, this study reports the importance of the adoption of field-based monitoring techniques when combined with a proper understanding and knowledge of the capabilities and limitations of each technique. [ABSTRACT FROM AUTHOR]

Objectives: To highlight the use of calibration weighting to improve the precision of estimates obtained from All of Us data and increase the return of value to communities from the All of Us Research Program., Materials and Methods: We used All of Us (2017-2022) data and raking to obtain prevalence estimates in two examples: discrimination in medical settings (N = 41 875) and food insecurity (N = 82 266). Weights were constructed using known population proportions (age, sex, race/ethnicity, region of residence, annual household income, and home ownership) from the 2020 National Health Interview Survey., Results: About 37% of adults experienced discrimination in a medical setting. About 20% of adults who had not seen a doctor reported being food insecure compared with 14% of adults who regularly saw a doctor., Conclusions: Calibration using raking is cost-effective and may lead to more precise estimates when analyzing All of Us data., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Proximal femoral fracture is common with a high mortality (7% mortality at 30 days). Accurate determination of mortality risk allows better consenting, clinical management and expectation management. Our study aim was to develop a prognostic tool to predict 30-day mortality after proximal femoral fracture, among patients treated within a dedicated hip fracture unit. Materials and methods: We collected data from our hospital concerning 2210 patients with 2287 proximal femoral fractures. The clinical parameters of 97 patients who died within 30 days of surgery were analysed. We used logistic regression to determine if the parameters' relationship with 30-day mortality was statistically significant or not. The statistically significant parameters were used to create a prognostic model for predicting 30-day mortality. Results: The 5 independent predictors of 30-day mortality were gender, age, admission source, preoperative Abbreviated Mental Test Score (AMTS) and American Society of Anesthesiologists Score (ASA). The highest risk was for males >85 years, admitted from institutional care, with low preoperative mental test score and high ASA grade. Using these predictors, we formulated the G4A score. The Hosmer-Lemeshow 'goodness of fit' test showed good concordance between observed and predicted mortality rates. Conclusions: We recommend the use of the G4A score to predict 30-day mortality after surgery for proximal femoral fracture, particularly within dedicated hip fracture units. Further research is needed to establish whether the findings of this study are applicable on a national scale. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To (1) evaluate an injury risk model that included modifiable and nonmodifiable factors into an arm injury risk prediction model in Minor League Baseball (MiLB) pitchers and (2) compare model performance separately for predicting the incidence of elbow and shoulder injuries. DESIGN: Prospective cohort. METHODS: A 10-year MiLB injury risk study was conducted. Pitchers were evaluated during preseason, and pitches and arm injuries were documented prospectively. Nonmodifiable variables included arm injury history, professional experience, arm dominance, year, and humeral torsion. Modifiable variables included BMI, pitch count, total range of motion, and horizontal adduction. We compared modifiable, nonmodifiable, and combined model performance by R², calibration (best = 1.00), and discrimination (area under the curve [AUC]; higher number is better). Sensitivity analysis included only arm injuries sustained in the first 90 days. RESULTS: In this study, 407 MiLB pitchers (141 arm injuries) were included. Arm injury incidence was 0.27 injuries per 1000 pitches. The arm injury model (calibration 1.05 [0.81-1.30]; AUC: 0.74 [0.69-0.80]) had improved performance compared to only using modifiable predictors (calibration: 0.91 [0.68-1.14]; AUC: 0.67 [0.62-0.73]) and only shoulder range of motion (calibration: 0.52 [0.29, 0.75]; AUC: 0.52 [0.46, 58]). Elbow injury model demonstrated improved performance (calibration: 1.03 [0.76-1.33]; AUC: 0.76 [0.69-0.83]) compared to the shoulder injury model (calibration: 0.46 [0.22-0.69]; AUC: 0.62 [95% CI: 0.55, 0.69]). The sensitivity analysis demonstrated improved model performance compared to the arm injury model. CONCLUSION: Arm injury risk is influenced by modifiable and nonmodifiable risk factors. The most accurate way to identify professional pitchers who are at risk for arm injury is to use a model that includes modifiable and nonmodifiable risk factors. [ABSTRACT FROM AUTHOR]

Despite long‐standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium‐scale (200–10,000 km2) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre‐storm) surface soil moisture (ASM) and the storm‐scale runoff coefficient (RC; the fraction of storm‐scale precipitation accumulation converted into streamflow). In humid and semi‐humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm‐scale RC observations (available only in basins that are both lightly regulated and gauged) with high‐quality ASM values (available quasi‐globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin‐wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean‐absolute error. Plain Language Summary: Accurately forecasting the fraction of rainfall that runs off into streams, as opposed to infiltrates into the soil, is critical for flash‐flood prediction, water‐resource monitoring, and tracking the transport of nutrients from agricultural fields into local waterways. Such forecasting is typically performed by hydrologic models that attempt to represent the physical processes responsible for surface runoff generation. However, to provide accurate streamflow forecasts, these models typically need to be calibrated against actual streamflow observations. This is problematic given the relatively poor, and declining, global availability of stream gauges. This paper presents a novel model calibration strategy that uses soil moisture from remote sensing and numerical modeling in place of streamflow observations during calibration. This transition has significant practical advantages because, unlike streamflow observations, the soil moisture data are continuously available across space. Our results demonstrate that this new approach can significantly improve hydrologic models within humid and semi‐humid basins lacking sufficient ground‐based instrumentation for traditional streamflow calibration. Key Points: Hydrologic models still perform poorly in ungauged basinsIn relative humid areas, the correlation between pre‐storm soil moisture and the storm‐scale runoff coefficient approaches unitySuch strong correlation can be exploited to improve the precision of runoff coefficient estimates provided by models in ungaged basins [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to highlight the value and key findings of on-site proton audits.Methods and Materials: The authors performed 38 on-site measurement-based peer reviews of proton centers participating in National Cancer Institute-funded clinical trials. The reviews covered beam calibration, lateral and depth measurements, mechanical checks, treatment planning and clinical practice, and quality assurance (QA) practices. Program deficiencies were noted, and recommendations were made about ways institutions could improve their practices.Results: Institutions received an average of 3 (range, 1-8) recommendations for practice improvements. The number of deficiencies did not decrease over time, highlighting the continued need for this type of peer review. The most common deficiencies were for Task Group-recommended QA compliance (97% of centers), computed tomography number (CTN) to relative linear stopping power conversion (59%), and QA procedures (53%). In addition, 32% of institutions assessed failed at least 1 lateral beam profile measurement (<90% of pixels passing 3% [global]/3 mm; 10% threshold), despite passing internal QA measurements. These failures occurred for several different plan configurations (large, small, shallow, and deep targets) and at different depths in the beam path (proximal to target, central, and distal). CTN to relative linear stopping power conversion curves showed deviations at low, mid, and high CTNs and highlighted areas of inconsistency between proton centers, with many centers falling outside of 2 sigma of the mean curve of their peers. All deficiencies from the peer review were discussed with the institutions, and many implemented dosimetric treatment planning and practice changes to improve the accuracy of their system and consistency with other institutions.Conclusions: This peer review program has been integral in confirming and promoting consistency and best practice across proton centers for clinical trials, minimizing deviations for outcomes data. [ABSTRACT FROM AUTHOR]

Stream permanence classifications (i.e., perennial, intermittent, ephemeral) are a primary consideration to determine stream regulatory status in the United States (U.S.) and are an important indicator of environmental conditions and biodiversity. However, at present, no models or products adequately describe surface water presence for regulatory determinations. We modified the Thornthwaite monthly water balance model (MWBM) with a flow threshold parameter to estimate flow permanence and evaluated the model's accuracy and precision for more than 1.3 million headwater stream reaches in the U.S. Pacific Northwest (PNW). Stream reaches were assigned to one of eight calibration groups by unsupervised classification based on sensitivity to MWBM parameters. Suitable MWBM parameter sets were identified by comparing modeled stream permanence estimates to surface water presence observations (SWPO). Parameter sets with accuracies > 65% were considered suitable. The MWBM estimated stream permanence with high precision at 40% of reaches, with poor precision at 20% of reaches, and no suitable parameter sets were identified for 40% of reaches. Results highlight the need for increased SWPO collection to improve calibration and assessment of stream permanence models. Additionally, implementation of the MWBM to estimate surface water presence indicates potential for process-based models to predict stream permanence with future development. [ABSTRACT FROM AUTHOR]

Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large‐sample SST assessment study empirically assesses how different data splitting methods influence post‐validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub‐periods (CSPs). A full‐period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35‐year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split‐sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split‐sample approach in hydrological modeling. Plain Language Summary: Hydrological model calibration is a critical model building process that infers key model parameter values from observed system response data. Conventionally, this process requires the historical period to be split into a calibration period for tuning parameters and a validation period for testing model robustness (i.e., the split‐sample). Unfortunately, there is a lack of empirical evidence supporting how exactly to define the split‐sample. We designed an exhaustive and novel experiment comparing the range of possible split‐sampling schemes, including calibrating to older/recent years, calibrating to a short/long period, and calibrating to the full period of available system response data. Each scheme was evaluated based on performance, assessed in three different ways, in numerous post‐validation model testing periods for each of the 926 calibration case studies (two different hydrological models applied in 463 catchments). Results show that using older data for model calibration and then using newer data for validation, which is the typical practice in the literature, is an inferior choice and should be avoided. The results also show that calibrating to the full historical data and skipping model validation entirely is the most robust choice. Therefore, the split‐sample approach applied in this community for decades should be revised. Key Points: A unique split‐sample experiment is performed across 463 catchments to provide guidance on split sample decision‐making in model calibrationCalibrating models to the full available data period and skipping model validation entirely is the most robust choiceCalibrating models to older data and then validating models on newer data, a very common approach in literature, is an inferior choice [ABSTRACT FROM AUTHOR]

Groundwater modules are critically important to the simulation of low flows in physically based land surface models (LSMs) and conceptual rainfall‐runoff models (HBV). Here, we develop a Groundwater for Ungauged Basins (GrUB) module that uses only physically based properties for which data are widely available, thus allowing its application without the need for calibration. GrUB is designed to be computationally simple and readily adaptable to a wide variety of LSMs and rainfall‐runoff models. We assess the performance of GrUB in 84 United States watersheds by incorporating it into HBV, a popular rainfall‐runoff model. We compare predictions of low flows by the native (calibrated) HBV groundwater module with those by the (uncalibrated) GrUB module and find that GrUB generates error metrics that are equivalent or superior to those generated by the (calibrated) HBV groundwater module. To assess whether predictions by GrUB are robust to changes in the structure and parameterization of the overlying hydrologic model, we run tests for two artificial scenarios: Slow Recharge with rates of percolation below 0.1 mm/day, and Fast Recharge with rates of percolation of up to 1,000 mm/day. GrUB proves to be robust to these extreme changes, with mean absolute error (MAE) of predictions of low flows only increasing by an average of up to 17%, while average MAE increases by up to 158% when the same tests are performed on HBV without the GrUB module. We suggest GrUB as a potential tool for improving predictions of low flows in LSMs, as well as rainfall‐runoff models when calibration data are sparse. Key Points: Develop a calibration‐free groundwater/low flow module that is compatible with land surface models and rainfall‐runoff modelsWithout calibration, the module's performance compares favorably with an existing, fully calibrated alternativeModule predictions of low flows are robust to drastic changes in the overlying hydrologic model [ABSTRACT FROM AUTHOR]

Objectives: To present the Real-World Progression In Diabetes (RAPIDS) 2.0 Risk Engine, the only simulation model to study the long-term trajectories of outcomes arising from dynamic sequences of glucose-lowering treatments in type 2 diabetes (T2DM)., Research Design and Methods: The RAPIDS model's risk equations were re-estimated using a Least Absolute Shrinkage and Selection Operator (LASSO)-based regularization of features that spanned baseline data from the last two quarters of current time and interactions with age. These equations were supplemented with estimates for the impact of dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose cotransporter-2 inhibitor classes of drugs as monotherapies and their combinations with metformin based on newer trial data and comprehensive meta-analyses. The probabilistic RAPIDS 2.0 model was calibrated (N = 25 000) and validated (N = 263 816) using electronic medical records (EMR) data between 2008 and 2021 from a national network of US healthcare organizations., Results: The EMR-based cohort had a mean age of 61 years at baseline, with 50% women, 70% non-Hispanic White individuals and 20% non-Hispanic Black individuals, and was followed for 17.5 quarters (range: 3-50). The final RAPIDS 2.0 risk engine accurately predicted the long-term trajectories of all nine biomarkers and nine outcomes in the hold-out validation sample. Similar accuracies in predictions were observed in each of the 14 subgroups studied., Conclusion: The RAPIDS 2.0 model demonstrated valid long-term predictions of outcomes in individuals with T2DM in the United States as a function of dynamic sequences of treatment use patterns. This highlights its potential to project long-term comparative effectiveness between alternative sequences of glucose-lowering treatment uses in the United States., (© 2024 John Wiley & Sons Ltd.)

Background: The Society of Thoracic Surgeons General Thoracic Surgery Database (STS-GTSD) previously reported short-term risk models for esophagectomy for esophageal cancer. We sought to update existing models using more inclusive contemporary cohorts, with consideration of additional risk factors based on clinical evidence., Methods: The study population consisted of adult patients in the STS-GTSD who underwent esophagectomy for esophageal cancer between January 2015 and December 2022. Separate esophagectomy risk models were derived for 3 primary end points: operative mortality, major morbidity, and composite morbidity or mortality. Logistic regression with backward selection was used, with predictors retained in models if P < .10. All derived models were validated using 9-fold cross-validation. Model discrimination and calibration were assessed for the overall cohort and specified subgroups., Results: A total of 18,503 patients from 254 centers underwent esophagectomy for esophageal cancer. Operative mortality, morbidity, and composite morbidity or mortality rates were 3.4%, 30.5%, and 30.9%, respectively. Novel predictors of short-term outcomes in the updated models included body surface area and insurance payor type. Overall discrimination was similar or superior to previous STS-GTSD models for operative mortality (C statistic = 0.72) and for composite morbidity or mortality (C statistic = 0.62), Model discrimination was comparable across procedure- and demographic-specific subcohorts. Model calibration was excellent in all patient subgroups., Conclusions: The newly derived esophagectomy risk models showed similar or superior performance compared with previous models, with broader applicability and clinical face validity. These models provide robust preoperative risk estimation and can be used for shared decision making, assessment of provider performance, and quality improvement., Competing Interests: Disclosures Robert Habib and Levi Bonnell report financial support was provided by The Society of Thoracic Surgeons. Robert Habib and Levi Bonnell report a relationship with The Society of Thoracic Surgeons that includes: employment. Biniam Kidane reports a relationship with BMS, Merck, AstraZeneca, Roche that includes advisory boards and speaker fees; with Olympus that includes consulting fees; with Medtronic that includes an unrestricted educational grant. The other authors have no conflicts of interest to disclose., (Copyright © 2024 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Introduction: The physical condition of the tip and the irradiance of LCUs used in dental offices in the United States and Canada was assessed twice., Methods: The tip irradiance and physical condition of the tip of 1067 LCUs from 544 dental clinics were assessed twice between 2015 and 2021 using the CheckMARC (BlueLight) radiometer. The irradiance values were compared by paired Student's t-test (α = 0.05)., Results: There was a wide range of irradiance values (200 to 3,777 mW/cm²). At the first assessment, only 26% of the LCUs had no visible damage or debris on the LCU tip. When retested up to 36 months later, 70% of the LCUs had their tip damage or debris issues entirely or partially resolved. 2.6% of the LCUs delivered an irradiance below 500 mW/cm² on the first and 1.6% on the second assessments. For the 5 brands that had a sufficient sample size, it was concluded that the irradiance increased when the tip damage or presence of tip debris was resolved., Conclusions: Initially, most LCUs (74%) had at least one problem related to damage or debris on the LCU tip. The irradiance increased when the tip damage or debris was resolved., (Copyright© 2024 Dennis Barber Ltd.)

The Cross-track Infrared Sounder (CrIS) is a Michelson-type Fourier Transform Spectrometer. The CrIS flight module 2 instrument was launched into orbit on November 18, 2017, onboard the NOAA-20 satellite as part of the United States (US) Joint Polar Satellite System (JPSS). The CrIS instrument measures the top-of-atmosphere upwelling spectral radiance in the thermal infrared (IR) spectrum. These measurements provide critical information for medium-range weather forecasting, and the retrieval of atmospheric profiles of temperature, water vapor, and other trace gases. The instrument noise equivalent radiance differential (NEdN) estimates are used by the weather forecasting systems, the trace gas atmospheric retrieval algorithms, and for trending the health and stability of the instrument over time. The current operational NEdN estimate is calculated using instrument observations from the deep space (DS) view and the internal calibration target (ICT). Two alternative methodologies are described here based on the principal component analysis (PCA) of an ensemble of calibrated Earth scene (ES) spectra. The NEdN calculation methods show that the instrument meets the specifications with a margin for all 27 detectors with an exception of one midwave IR (MWIR) field-of-view (FOV) 9, which is borderline. The PCA analysis shows that warmer ES spectra have higher noise, known as scene shot, for the short-wave IR (SWIR) band. Using the PCA analysis, the NEdN for the long-wave IR FOV 5 is 30% higher than the NEdN calculated by the operational algorithm. Correlated noise is also found due to the effect of the instrument self-apodization correction. [ABSTRACT FROM AUTHOR]

Purpose: The objective of our study was to investigate the epidemiologic characteristics and prognostic factors in patients with pulmonary acinar cell carcinoma (PACC). Methods: PACC patients diagnosed between 1975 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The trend in PACC incidence was assessed using joinpoint regression software. Overall survival (OS) and disease‐specific survival (DSS) were evaluated using the Kaplan–Meier method and log‐rank test. Univariate and multivariate Cox regression analysis was performed to identify the independent prognostic factors for OS and DSS. Nomograms to predict survival possibilities were constructed based on the identified independent prognostic factors. Results: A total of 2918 patients were identified with PACC. The mean age was 65.2 ± 8.95 years with a female to male of 1.6:1. The incidence of PACC steadily increased by an annual percentage change (APC) of 3.2% (95% CI 2.1–4.4, p < 0.05). Multivariate Cox regression analysis revealed that age, gender, race, stage, grade, tumor size, number of positive lymph nodes, surgery, and chemotherapy were independent prognostic factors for survival. Nomograms specifically for PACC were constructed to predict 1‐ and 5‐year OS and DSS possibility, respectively. The concordance index (C‐index) and calibration plots showed the established nomograms had robust and accurate performance. Conclusion: PACC was rare but the incidence has been steadily increasing over the past four decades. Survival has improved in recent years. Surgery or chemotherapy could provide better OS and DSS. The established nomograms specifically for PACC were robust and accurate in predicting 1‐ and 5‐year OS and DSS. [ABSTRACT FROM AUTHOR]

Background: Two models, the Help with the Assessment of Adenopathy in Lung cancer (HAL) and Help with Oncologic Mediastinal Evaluation for Radiation (HOMER), were recently developed to estimate the probability of nodal disease in patients with non-small cell lung cancer (NSCLC) as determined by endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA). The objective of this study was to prospectively externally validate both models at multiple centers.Research Question: Are the HAL and HOMER models valid across multiple centers?Study Design and Methods: This multicenter prospective observational cohort study enrolled consecutive patients with PET-CT clinical-radiographic stages T1-3, N0-3, M0 NSCLC undergoing EBUS-TBNA staging. HOMER was used to predict the probability of N0 vs N1 vs N2 or N3 (N2|3) disease, and HAL was used to predict the probability of N2|3 (vs N0 or N1) disease. Model discrimination was assessed using the area under the receiver operating characteristics curve (ROC-AUC), and calibration was assessed using the Brier score, calibration plots, and the Hosmer-Lemeshow test.Results: Thirteen centers enrolled 1,799 patients. HAL and HOMER demonstrated good discrimination: HAL ROC-AUC = 0.873 (95%CI, 0.856-0.891) and HOMER ROC-AUC = 0.837 (95%CI, 0.814-0.859) for predicting N1 disease or higher (N1|2|3) and 0.876 (95%CI, 0.855-0.897) for predicting N2|3 disease. Brier scores were 0.117 and 0.349, respectively. Calibration plots demonstrated good calibration for both models. For HAL, the difference between forecast and observed probability of N2|3 disease was +0.012; for HOMER, the difference for N1|2|3 was -0.018 and for N2|3 was +0.002. The Hosmer-Lemeshow test was significant for both models (P = .034 and .002), indicating a small but statistically significant calibration error.Interpretation: HAL and HOMER demonstrated good discrimination and calibration in multiple centers. Although calibration error was present, the magnitude of the error is small, such that the models are informative. [ABSTRACT FROM AUTHOR]

Background/Objectives: No data exist regarding the validity of International Classification of Disease (ICD)‐10 dementia diagnoses against a clinician‐adjudicated reference standard within Medicare claims data. We examined the accuracy of claims‐based diagnoses with respect to expert clinician adjudication using a novel database with individual‐level linkages between electronic health record (EHR) and claims. Design: In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims‐based definitions of dementia against the reference standard. Setting: Mass‐General‐Brigham Healthcare (MGB), Massachusetts, USA. Participants: From an eligible population of 40,690 fee‐for‐service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates. Intervention: None. Measurements: We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration‐in‐the‐large [CITL] and calibration slope) of three ICD‐10 claims‐based definitions of dementia against clinician‐adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10‐fold cross‐validation (CV). Results: Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non‐Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best‐performing definition demonstrated excellent accuracy (CV‐AUC 0.94; 95% CI 0.92–0.96) and was well‐calibrated to the reference standard of clinician‐adjudicated dementia (CV‐CITL <0.001, CV‐slope 0.97). Conclusion: This study is the first to validate ICD‐10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real‐world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD‐9 definition of dementia. [ABSTRACT FROM AUTHOR]

The two-parameter recursive digital filter method (Eckhardt) and the conductivity mass balance (CMB) method are two widely used baseflow separation methods favored by hydrologists. Some divergences in the application of these two methods have emerged in recent years. Some scholars believe that deviation of baseflow separation results of the two methods is due to uncertainty of the parameters of the Eckhardt method and that the Eckhardt method should be corrected by reference to the CMB method. However, other scholars attribute the deviation to the fact that they contain different transient water components. This study aimed to resolve this disagreement by analyzing the effectiveness of the CMB method for correcting the Eckhardt method through application of the methods to 26 basins in the United States by comparison of the biases between the generated daily baseflow series. The results showed that the approach of calibrating the Eckhardt method against the CMB method provides a "false" calibration of total baseflow by offsetting the inherent biases in the baseflow sequences generated by the two methods. The baseflow sequence generated by the Eckhardt method usually includes slow interflow and bank storage return flow, whereas that of the CMB method usually includes high-conductivity water flushed from swamps and depressions by rainfall, but not low-conductivity interflow and bank storage return flow. This difference results in obvious peak misalignment and periodic deviation between the baseflow sequences obtained by the two methods, thereby preventing calibration. However, multi-component separation of streamflow can be achieved through comparison. Future research should recognize the deviations between the separation results obtained by the different methods, identify the reasons for these differences, and explore the hydrological information contained therein. [ABSTRACT FROM AUTHOR]

Hydrological models are useful tools for exploring the impact of climate change. To prioritize parameters for calibration and to evaluate hydrological model functioning, sensitivity analysis can be conducted. Parameter sensitivity, however, varies over climate, and therefore climate change could influence parameter sensitivity. In this study we explore the change in parameter sensitivity for the mean discharge and the timing of the discharge, within a plausible climate change rate. We investigate whether changes in sensitivity propagate into the calibration strategy and diagnostically compare three hydrological models based on the sensitivity results. We employed three frequently used hydrological models (SAC, VIC, and HBV) and explored parameter sensitivity changes across 605 catchments in the United States by comparing GCM(RCP8.5)-forced historical and future periods. Consistent among all hydrological models and both for the mean discharge and the timing of the discharge is that the sensitivity of snow parameters decreases in the future. Which other parameters increase in sensitivity is less consistent among the hydrological models. In 45 % to 55 % of the catchments, dependent on the hydrological model, at least one parameter changes in the future in the top-5 most sensitive parameters for mean discharge. For the timing, this varies between 40 % and 88 %. This requires an adapted calibration strategy for long-term projections, for which we provide several suggestions. The disagreement among the models on the processes that become more relevant in future projections also calls for a strict evaluation of the adequacy of the model structure for long-term simulations. [ABSTRACT FROM AUTHOR]

Objective: This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation., Materials and Methods: We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases. We use a train-test split of 75%/25% and evaluate performance with discrimination and calibration. Statistical analysis for difference in performance uses Friedman's test and critical difference diagrams., Results: Of the 840 models we develop, L1 and ElasticNet emerge as superior in both internal and external discrimination, with a notable AUC difference. BAR and IHT show the best internal calibration, without a clear external calibration leader. ElasticNet typically has larger model sizes than L1. Methods like IHT and BAR, while slightly less discriminative, significantly reduce model complexity., Conclusion: L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Coronary artery bypass graft (CABG) surgery is a focus of bundled and alternate payment models that capture outcomes up to 90 days postsurgery. While clinical registry risk models perform well, measures encompassing mortality beyond 30 days do not currently exist. We aimed to develop a risk-adjusted hospital-level 90-day all-cause mortality measure intended for assessing hospital performance in payment models of CABG surgery using administrative data.Methods: Building upon Centers for Medicare and Medicaid Services hospital-level 30-day all-cause CABG mortality measure specifications, we extended the mortality timeframe to 90 days after surgery and developed a new hierarchical logistic regression model to calculate hospital risk-standardized 90-day all-cause mortality rates for patients hospitalized for isolated CABG. The model was derived from Medicare claims data for a 3-year cohort between July 2014 to June 2017. The data set was randomly split into 50:50 development and validation samples. The model performance was evaluated with C statistics, overfitting indices, and calibration plot. The empirical validity of the measure result at the hospital level was evaluated against the Society of Thoracic Surgeons composite star rating.Results: Among 137 819 CABG procedures performed in 1183 hospitals, the unadjusted mortality rate within 30 and 90 days were 3.1% and 4.7%, respectively. The final model included 27 variables. Hospital-level 90-day risk-standardized mortality rates ranged between 2.04% and 11.26%, with a median of 4.67%. C statistics in the development and validation samples were 0.766 and 0.772, respectively. We identified a strong positive correlation between 30- and 90-day risk-standardized mortality rates, with a regression slope of 1.09. Risk-standardized mortality rates also showed a stepwise trend of lower 90-day mortality with higher Society of Thoracic Surgeons composite star ratings.Conclusions: We present a measure of hospital-level 90-day risk-standardized mortality rates following isolated CABG. This measure complements Centers for Medicare and Medicaid Services' existing 30-day CABG mortality measure by providing greater insight into the postacute recovery period. It offers a balancing measure to ensure efforts to reduce costs associated with CABG recovery and rehabilitation do not result in unintended consequences. [ABSTRACT FROM AUTHOR]

Proton therapy is an expanding radiotherapy modality in the United States and worldwide. With the number of proton therapy centers treating patients increasing, so does the need for consistent, high‐quality clinical commissioning practices. Clinical commissioning encompasses the entire proton therapy system's multiple components, including the treatment delivery system, the patient positioning system, and the image‐guided radiotherapy components. Also included in the commissioning process are the x‐ray computed tomography scanner calibration for proton stopping power, the radiotherapy treatment planning system, and corresponding portions of the treatment management system. This commissioning report focuses exclusively on intensity‐modulated scanning systems, presenting details of how to perform the commissioning of the proton therapy and ancillary systems, including the required proton beam measurements, treatment planning system dose modeling, and the equipment needed. [ABSTRACT FROM AUTHOR]

Purpose: Caregivers of service members/veterans (SMVs) encounter a number of barriers when navigating the military health care system. The purpose of this study was to develop a new measure to assess potential caregiver frustration with the systems of care and benefits in the United States Departments of Defense and Veterans Affairs. Method: The TBI-CareQOL Military Health Care Frustration measure was developed using data from 317 caregivers of SMVs with TBI who completed an item pool comprised of 64 questions pertaining to anger or frustration with accessing military health care services. Results: Exploratory and confirmatory factor analyses supported the retention of 58 items. Constrained graded response model (GRM) overall fit and item fit analyses and differential item functioning investigations of age and education factors supported the retention of 43 items in the final measure. Expert review and GRM item calibration products were used to inform the selection of two 6-item static short forms (TBI-CareQOL Military Health Care Frustration-Self; TBI-CareQOL Military Health Care Frustration-Person with TBI) and to program the TBI-CareQOL Military Health Care Frustration computer adaptive test (CAT). Preliminary data supported the reliability (i.e., internal consistency and test-retest reliability) as well as the validity (i.e., convergent, discriminant, and known-groups) of the new measure. Conclusions: The new TBI-CareQOL Military Health Care Frustration measure can be used to examine caregiver perceptions of and experience with the military health care system, to target improvements. [ABSTRACT FROM AUTHOR]

The use of acoustics for determining tree stiffness can be helpful to improve end product value. However, methods to monitor equipment calibration in the field is limited. This experiment endeavored to test the hypothesis that ASTM D198 is an alternative way to monitor calibration performance in the field. ASTM D198 is a standard test method to test the static modulus of elasticity of structural lumber and is a common test in many local sawmills within the United States. Southern pine lumber (Pinus spp.) was first tested at a local sawmill for modulus of elasticity as per ASTM D198-14. Then acoustic probes were placed 7 feet apart in nominal 2x4 lumber and correlation equations between lumber stiffness and acoustic speed was developed. We then built regression curves for each machine using the lumber derived data, but applied it onto trees. This study found that all three machines gave a statistically equal reading to the reference machine after readjustment with ASTM D198 and supported the hypothesis that the ASTM standard could be an additional monitoring procedure in the field. Meanwhile, we do caution that this standard not be used as a calibration substitution but just as a monitoring check. We feel more studies are warrantied where the log being tested is harvested and converted to small clears or lumber, and then a similar study is performed. [ABSTRACT FROM AUTHOR]

While the physical processes governing groundwater flow are well understood, and the computational resources now exist for solving the governing equations in three dimensions over continental‐scale domains, there remains substantial uncertainty about the subsurface distribution of the properties that control groundwater flow and transport for much of the contiguous United States (CONUS). The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base‐flow discharge, but is not well‐characterized at large regional to continental scales. We used a process‐based inversion of CONUS‐extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base‐flow, and (d) long‐term average water content in the unsaturated zone. CONUS‐extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long‐term average transmissivity for each subdomain model calibrated against water‐levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first‐order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long‐term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways. Plain Language Summary: We developed a simulation of the shallow groundwater system for the contiguous United States (CONUS) and compared it to large data sets of water table measurements in order to generate a map of surficial transmissivity, which is a property that controls the flow of water between its entrance into the groundwater system and its discharge to streams and rivers. Better maps of this property are one ingredient to improved understanding of how the landscape stores and releases water at large spatial scales. Key Points: Calibrated simulations of the shallow groundwater system were used to estimate the CONUS‐extent spatial distribution of transmissivityCorrelations were explored between the estimated transmissivities and recharge, elevation, and topographic slopeSimulated depth to water and groundwater discharge were used to derive unsaturated zone properties and long‐term average base‐flow on the NHDPlusV2 stream network [ABSTRACT FROM AUTHOR]

This paper introduces for the first time the concept of Bayesian model averaging (BMA) with multiple prior structures, for rainfall‐runoff modeling applications. The original BMA model proposed by Raftery et al. (2005, https://doi.org.10.1175/MWR2906.1) assumes that the prior probability density function (pdf) is adequately described by a mixture of Gamma and Gaussian distributions. Here we discuss the advantages of using BMA with fixed and flexible prior distributions. Uniform, Binomial, Binomial‐Beta, Benchmark, and Global Empirical Bayes priors along with Informative Prior Inclusion and Combined Prior Probabilities were applied to calibrate daily streamflow records of a coastal plain watershed in the southeast United States. Various specifications for Zellner's g prior including Hyper, Fixed, and Empirical Bayes Local (EBL) g priors were also employed to account for the sensitivity of BMA and derive the conditional pdf of each constituent ensemble member. These priors were examined using the simulation results of conceptual and semidistributed rainfall‐runoff models. The hydrologic simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each model. BMA was then used to subsequently combine the simulations of the posterior pdf of each constituent hydrological model. Analysis suggests that a BMA based on combined fixed and flexible priors provides a coherent mechanism and promising results for calculating a weighted posterior probability compared to individual model calibration. Furthermore, the probability of Uniform and Informative Prior Inclusion priors received significantly lower predictive error, whereas more uncertainty resulted from a fixed g prior (i.e., EBL). Plain Language Summary: This study presents a two‐step procedure that includes model calibration of a range of hydrological models using DREAM (zs) algorithm, followed by ensemble prediction of streamflow using Bayesian model averaging (BMA) with various prior structures. The hydrological modeling simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each hydrologic model simulation. BMA was then used to subsequently combine the simulations on the most important parts of the posterior probabilities of each constituent hydrological model. Analysis suggests a BMA with fixed and flexible priors provides a coherent mechanism and promising results for calibrating a weighted posterior probability compared to individual model calibration. The hierarchy of prior distributions used in this study increased the flexibility of BMA fitting for daily streamflow simulation and reduced the dependence of posterior and predictive uncertainty (including model probabilities) on prior assumptions of hydrological modeling simulation. Key Points: Bayesian model averaging with fixed and flexible prior structures were applied to combine the posterior probability distribution of four hydrological modelsCustom prior inclusion and uniform prior induced a much sharper posterior medianPutting a prior on both θ and g makes the analysis naturally adaptive and avoids the information paradox [ABSTRACT FROM AUTHOR]

Background and aims: The focus of this paper is on the improvement of substance use disorder (SUD) screening and measurement. Using a multi‐dimensional item response theory model, the bifactor model, we provide a psychometric harmonization between SUD, depression, anxiety, trauma, social isolation, functional impairment and risk‐taking behavior symptom domains, providing a more balanced view of SUD. The aims are to (1) develop the item‐bank, (2) calibrate the item‐bank using a bifactor model that includes a primary dimension and symptom‐specific subdomains, (3) administer using computerized adaptive testing (CAT) and (4) validate the CAT‐SUD in Spanish and English in the United States and Spain. Design Item bank construction, item calibration phase, CAT‐SUD validation phase. Setting: Primary care, community clinics, emergency departments and patient‐to‐patient referrals in Spain (Barcelona and Madrid) and the United States (Boston and Los Angeles). Participants/cases: Calibration phase: the CAT‐SUD was developed via simulation from complete item responses in 513 participants. Validation phase: 297 participants received the Composite International Diagnostic Interview (CIDI) and the CAT‐SUD. Measurements A total of 252 items from five subdomains: (1) SUD, (2) psychological disorders, (3) risky behavior, (4) functional impairment and (5) social support. CAT‐SUD scale scores and CIDI SUD diagnosis. Findings Calibration: the bifactor model provided excellent fit to the multi‐dimensional item bank; 168 items had high loadings (> 0.4 with the majority > 0.6) on the primary SUD dimension. Using an average of 11 items (four to 26), which represents a 94% reduction in respondent burden (average administration time of approximately 2 minutes), we found a correlation of 0.91 with the 168‐item scale (precision of 5 points on a 100‐point scale). Validation: strong agreement was found between the primary CAT‐SUD dimension estimate and the results of a structured clinical interview. There was a 20‐fold increase in the likelihood of a CIDI SUD diagnosis across the range of the CAT‐SUD (AUC = 0.85). Conclusions: We have developed a new approach for the screening and measurement of SUD and related severity based on multi‐dimensional item response theory. The bifactor model harmonized information from mental health, trauma, social support and traditional SUD items to provide a more complete characterization of SUD. The CAT‐SUD is highly predictive of a current SUD diagnosis based on a structured clinical interview, and may be predictive of the development of SUD in the future. [ABSTRACT FROM AUTHOR]

Objective: The goal of the study was to develop and validate a prediction model for cesarean delivery after labor induction that included factors known before the start of induction, unlike prior studies that focused on characteristics at the time of induction. Materials and Methods: Using 17,370 term labor inductions without documented medical indications occurring at 14 U.S. hospitals, 2007–2012, we created and evaluated a model predicting cesarean delivery. We assessed model calibration and discrimination, and we used bootstrapping for internal validation. We externally validated the model by using 2122 labor inductions from a hospital not included in the development cohort. Results: The model contained eight variables—gestational age, maternal race, parity, maternal age, obesity, fibroids, excessive fetal growth, and history of herpes—and was well calibrated with good risk stratification at the extremes of predicted probability. The model had an area under the curve (AUC) for the receiver operating characteristic curve of 0.82 (95% confidence interval 0.81–0.83), and it performed well on internal validation. The AUC in the external validation cohort was 0.82. Conclusion: This prediction model can help providers estimate a woman’s risk of cesarean delivery when planning a labor induction. [ABSTRACT FROM AUTHOR]

Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales. [ABSTRACT FROM AUTHOR]

Background: Early hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.Aims: We leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.Methods: A national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.Results: We analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.Conclusion: Our model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination. [ABSTRACT FROM AUTHOR]

Crop model calibration refers to the process where values are obtained for a subset of model parameters that represent cultivar traits. Calibrated parameters, however, are just as likely to compensate for limitations in model structure as well as reflect true phenotypic characteristics. This confounding of genetics with production environment limits model accuracy particularly for climate assessments and can result in crop parameter values that are location dependent. We evaluated the calibration stability of the potato (Solanum tuberosum L.) model SPUDSIM for two cultivars grown in two sites in the United States sites using a location specific (R1) and cross‐location (R2) methodology. Differences between R1 coefficient values ranged from 4 to 17% between the two sites depending on cultivar. This likely reflected over‐coupling of canopy expansion rate within the model structure as well as an over‐sensitivity to temperature and photoperiod. Broader site differences were observed for growth coefficients, particularly when compared with the R2 method. Validation year end‐of‐season yields (17% error or less) and in‐season RMSE (<36.1 g plant−1) were similar for both methods. The R2 calibration overpredicted yields by 5% or less as compared with R1, with a maximum discrepancy of 0.35 Mg ha−1 in climate forecasted impacts. Consistent differences between R1 and R2 calibrated models in rates of yield decline per decade were not observed for cultivar, location, or climate scenario, and averaged 0.034 Mg ha−1 yr−1 for R1 and 0.038 for R2. Thus, calibration for these cultivars and locations was relatively stable in SPUDSIM for the temperate climate evaluated. [ABSTRACT FROM AUTHOR]

Background. Portal vein tumor thrombosis (PVTT) is one of the major predictive factors for patients with hepatocellular carcinoma (HCC). The objective of this study was to establish a prognostic nomogram for identifying individual survival outcomes in patients with HCC and PVTT on conservative treatment based on specific factors. Methods. Two hundred and ten patients with HCC and PVTT on conservative treatment in Beijing Ditan Hospital between June 2008 and May 2017 were studied retrospectively as a derivation cohort. We built a nomogram based on independent risk factors for survival prediction. The concordance index (c-index) and a calibration curve were used to evaluate the predictive accuracy. During the study, 102 patients were included at the Putuo Hospital and Third People's Hospital of Changzhou as a validation cohort. Results. In the derivation cohort, the independent factors for overall survival were identified by multivariate analysis, namely, aspartate aminotransferase ≥119 IU/L, gamma-glutamyl transferase ≥115 IU/L, Child–Pugh class C liver function, creatinine ≥91 μmoI/L, α-fetoprotein ≥400 ng/ml, and largest tumor diameter ≥5 cm. The nomogram had a c-index of 0.737 (95% confidence interval, 0.692–0.782) and the calibration curves fitted well. The median survival time was 4.2 months in the derivation cohort, with an MST of 5 months for BCLC C stage and 1.8 months for BCLC D stage patients. Kaplan–Meier analysis showed significant statistical differences in the 6-month overall survival rates of the primary and validation cohorts after the total scores were divided into three quartiles (low risk: 0–85; intermediate risk: 86–210; high risk: ≥211; p < 0.0001 in both cohorts). Conclusions. The nomogram can be a more accurate and individualized prediction for 6-month overall survival of patients with HCC and PVTT on conservative treatment, and it is possible to consider further active interventions for patients in the low-risk group (0–85 scores) to achieve the aim of prolonging survival. [ABSTRACT FROM AUTHOR]

In the calibration of hydrological models, evaluation criteria are explicitly and quantitatively defined as single- or multi-objective functions when utilizing automatic calibration approaches. In most previous studies, there is a general opinion that no single-objective function can represent all important characteristics of even one specific hydrological variable (e.g., streamflow). Thus hydrologists must turn to multi-objective calibration. In this study, we demonstrated that an optimized single-objective function can compromise multi-response modes (i.e., multi-objective functions) of the hydrograph, which is defined as summation of a power function of the absolute error between observed and simulated streamflow with the exponent of power function optimized for specific watersheds. The new objective function was applied to 196 model parameter estimation experiment (MOPEX) watersheds across the eastern United States using the semi-distributed Xinanjiang hydrological model. The optimized exponent value for each watershed was obtained by targeting four popular objective functions focusing on peak flows, low flows, water balance, and flashiness, respectively. Results showed that the optimized single-objective function can achieve a better hydrograph simulation compared to the traditional single-objective function Nash-Sutcliffe efficiency coefficient for most watersheds, and balance high flow part and low flow part of the hydrograph without substantial differences compared to multi-objective calibration. The proposed optimal single-objective function can be practically adopted in the hydrological modeling if the optimal exponent value could be determined a priori according to hydrological/climatic/landscape characteristics in a specific watershed. [ABSTRACT FROM AUTHOR]

Conventional basin‐by‐basin approaches to calibrate hydrologic models are limited to gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density in space falls seriously behind the need from present‐day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of the Variable Infiltration Capacity (VIC) model at every 1/8° grid‐cell using machine learning‐based maps of four streamflow characteristics for the conterminous United States (CONUS), with a total of 52,663 grid‐cells. This new calibration approach, as an alternative to parameter regionalization, applied to ungauged regions too. A key difference made here is that we tried to regionalize physical variables (streamflow characteristics) instead of model parameters whose behavior may often be less well understood. The resulting parameter fields no longer presented any spatial discontinuities and the patterns corresponded well with climate characteristics, such as aridity and runoff ratio. The calibrated parameters were evaluated against observed streamflow from 704/648 (calibration/validation period) small‐to‐medium‐sized catchments used to derive the streamflow characteristics, 3941/3809 (calibration/validation period) small‐to‐medium‐sized catchments not used to derive the streamflow characteristics as well as five large basins. Comparisons indicated marked improvements in bias and Nash‐Sutcliffe efficiency. Model performance was still poor in arid and semiarid regions, which is mostly due to both model structural and forcing deficiencies. Although the performance gain was limited by the relative small number of parameters to calibrate, the study and results here served as a proof‐of‐concept for a new promising approach for fine‐scale hydrologic model calibrations. Key Points: Novel method proposed for hydrologic model calibration using machine learning‐based maps of streamflow characteristics1/8° grid‐by‐grid calibration resulting in parameters with good spatial coherenceCalibrated parameters substantially improved model performance in runoff prediction [ABSTRACT FROM AUTHOR]

In the bridge design specifications of the American Association of State Highway and Transportation Officials using the Load and Resistance Factor Design method, the loads and resulting force effects are given two-letter designations, e.g. "SE" for "force effects due to settlement". The same two-letter designation is used as a subscript to distinguish load factors, γ, to be applied to loads and other actions acting on the bridge. Thus, the load factor γSE is used to develop factored values of the additional induced force effects such as moments and shears in a bridge structure due to foundation movements. This paper presents the process to calibrate the SE load factor as a function of the target reliability index based on structural service limit states such as cracking in a bridge superstructure. In this process, the target reliability index, uncertainty in predicted movements, and user-specified deterministic tolerable movements are considered in a unified manner. The calibration process can be used for any analytical method and any pattern of movement. The calibration process is demonstrated by an example of immediate settlement for five analytical methods using a dataset based on 20 instrumented spread footings from 10 bridges in the northeast USA. [ABSTRACT FROM AUTHOR]