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Topic models are mathematical and statistical models used to analyze textual data. The objective of topic models is to gain information about the latent semantic space of a set of related textual data. The semantic space of a set of textual data contains the relationship between documents and words and how they are used. Topic models are becoming more common in educational measurement research as a method for analyzing students' responses to constructed-response items. Two popular topic models are latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). LSA uses linear algebra techniques, whereas LDA uses an assumed statistical model and generative process. In educational measurement, LSA is often used in algorithmic scoring of essays due to its high reliability and agreement with human raters. LDA is often used as a supplemental analysis to gain additional information about students, such as their thinking and reasoning. This article reviews and compares the LSA and LDA topic models. This article also introduces a methodology for comparing the semantic spaces obtained by the two models and uses a simulation study to investigate their similarities.

A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's information criterion (DIC), sample size adjusted BIC (SABIC), relative entropy, the integrated classification likelihood criterion (ICL-BIC), the adjusted Lo-Mendell-Rubin (LMR), and Vuong-Lo-Mendell-Rubin (VLMR). The accuracy of the fit indices was assessed for correct detection of the number of latent classes for different simulation conditions including sample size (2,500 and 5,000), test length (15, 30, and 45), mixture proportions (equal and unequal), number of latent classes (2, 3, and 4), and latent class separation (no-separation and small separation). Simulation study results indicated that as the number of examinees or number of items increased, correct identification rates also increased for most of the indices. Correct identification rates by the different fit indices, however, decreased as the number of estimated latent classes or parameters (i.e., model complexity) increased. Results were good for BIC, CAIC, DIC, SABIC, ICL-BIC, LMR, and VLMR, and the relative entropy index tended to select correct models most of the time. Consistent with previous studies, AIC and AICc showed poor performance. Most of these indices had limited utility for three-class and four-class mixture 3PL model conditions.

Integrated writing assessments are tasks that require students to construct an essay using evidence from source texts. Topic models are statistical models used to analyze text. This study analyzes two integrated writing tasks from students in grades 6 (N=1,250) and 8 (N=1,250) using topic models. Independent evaluations of the essays were also conducted to help generalize the results across the two grades and better understand students' underlying writing techniques. Four main categories were identified: (1) use of words that appeared in the prompt, (2) words that appeared in the source texts, (3) words that reflected inference, and (4) words that reflected integration. Higher-scoring students used words that reflect inference and integration of source text more often than lower-performing students.

Scholars and practitioners have called for personalized and widely accessible professional development (PD) for teachers. Yet, a long-standing tension between customizing support and increasing access to such support has hindered the scale-up of high-quality PD for individual teachers. This study addresses this challenge by developing a computerized program for middle school mathematics teachers that provides frequent opportunities for teachers to interact with and obtain personalized and real-time feedback from a virtual facilitator based on natural language processing. Based on the data collected from 1727 middle school students in an experiment in which the teachers of these students were randomly assigned to the program or the business-as-usual condition (i.e., the control group), we found that the program had a statistically significant impact on students' mathematics performance. These results demonstrate the potential of incorporating an automated, interactive feedback tool supported by artificial intelligence to create effective, scalable teacher PD.

Abstract Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the ensemble. The former approach runs into practical difficulties, as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape, whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network, trained efficiently through a physics-informed semi-generative approach. In this work, we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil, we sacrifice some photons but in return we achieve much enhanced sidelobes and, hence, higher sensitivity to the change of the size distribution. The result is a 60 × reduction in total acquisition and processing time, or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption, it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying, blending, and other chemical and pharmaceutical manufacturing processes.

Summary: Background: People with disabilities are at elevated risk of adverse short-term outcomes following hospitalization for acute infectious illness. No prior studies have compared long-term healthcare use among this high-risk population. We compared the healthcare use of adults with disabilities in the one year following hospitalization for COVID-19 vs. sepsis vs. influenza. Methods: We performed a population-based cohort study using linked clinical and health administrative databases in Ontario, Canada of all adults with pre-existing disability (physical, sensory, or intellectual) hospitalized for COVID-19 (n = 22,551, median age 69 [IQR 57–79], 47.9% female) or sepsis (n = 100,669, median age 77 [IQR 66–85], 54.8% female) between January 25, 2020, and February 28, 2022, and for influenza (n = 11,216, median age 78 [IQR 67–86], 54% female) or sepsis (n = 49,326, median age 72 [IQR 62–82], 45.8% female) between January 1, 2014 and March 25, 2019. The exposure was hospitalization for laboratory-confirmed SARS-CoV-2 or influenza, or sepsis (not secondary to COVID-19 or influenza). Outcomes were ambulatory care visits, diagnostic testing, emergency department visits, hospitalization, palliative care visits and death within 1 year. Rates of these outcomes were compared across exposure groups using propensity-based overlap weighted Poisson and Cox proportional hazards models. Findings: Among older adults with pre-existing disability, hospitalization for COVID-19 was associated with lower rates of ambulatory care visits (adjusted rate ratio (aRR) 0.88, 95% confidence interval (CI), 0.87–0.90), diagnostic testing (aRR 0.86, 95% CI, 0.84–0.89), emergency department visits (aRR 0.91, 95% CI, 0.84–0.97), hospitalization (aRR 0.74, 95% CI, 0.71–0.77), palliative care visits (aRR 0.71, 95% CI, 0.62–0.81) and low hazards of death (adjusted hazard ratio (aHR) 0.71, 95% 0.68–0.75), compared to hospitalization for sepsis during the COVID-19 pandemic. Rates of healthcare use among those hospitalized for COVID-19 varied compared to those hospitalized for influenza or sepsis prior to the pandemic. Interpretation: This study of older adults with pre-existing disabilities hospitalized for acute infectious illness found that COVID-19 was not associated with higher rates of healthcare use or mortality over the one year following hospital discharge compared to those hospitalized for sepsis. However, hospitalization for COVID-19 was associated with higher rates of ambulatory care use and mortality when compared to influenza. As COVID-19 enters an endemic phase, the associated long-term health resource use and risks in the contemporary era are reassuringly similar to sepsis and influenza, even among people with pre-existing disabilities. Funding: This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and the Ministry of Long-Term Care. This study also received funding from the Canadian Institutes of Health Research (CIHR GA4-177772).

BackgroundThe triglyceride (TG) content of low-density lipoprotein (LDL-TG) has been shown to be more predictive of atherosclerotic cardiovascular disease (ASCVD) events than the cholesterol content of LDL (LDL-C). The goal of our study was to develop an equation for estimating LDL-TG (eLDL-TG) based on the standard lipid panel and to compare it to estimated LDL-C as an ASCVD risk biomarker.MethodsUsing least-square regression analysis, the following eLDL-TG equation was developed: eLDL-TG=TG38.5+NonHDL-C5.75+9.75TGNonHDL-C+244HDL-C−2.95. LDL-TG was measured by the β-quantification (BQ) reference method (N = 40,202). LDL-C was calculated by the Sampson-NIH equation. The association of LDL-C and eLDL-TG with ASCVD risk markers was performed in the National Heart and Nutrition Examination Survey (NHANES) (N = 37,053) and with ASCVD events in a primary prevention cohort from the UK Biobank (UKB) (N = 429,367) and the Atherosclerosis Risk in Communities (ARIC) study (N = 14,632).ResultseLDL-TG showed better ASCVD risk stratification of UKB participants than LDL-C (Wilcoxon Chi-Square: 2,099.6 vs. 418.7, respectively). Receiving-operating characteristics analysis revealed that eLDL-TG had a stronger association with ASCVD events than LDL-C (AUC: 0.596 vs. 0.542, respectively) and other conventional lipid markers. Similar findings were found in ARIC. Discordance analysis in UKB showed that the group with low LDL-C/high eLDL-TG had a similar risk as the high LDL-C/high eLDL-TG group. Furthermore, these same two groups with the highest eLDL-TG levels and the highest ASCVD event rate also had higher mean levels of systolic blood pressure, Body Mass Index, hemoglobin A1C, and C-reactive protein than the two lower eLDL-TG groups. Using eLDL-TG > 44.6 mg/dl (80th percentile) as a cut-point leads to a hazard ratio of 1.32 (95% CI, 1.29–1.36) for ASCVD events, which remained significant after adjustment for LDL-C and apoB. Furthemore, using eLDL-TG as a risk-enhancer test leads to reclassification of 50% more high-risk individuals than current lipid-enhancer test rules.ConclusionsLike LDL-C, LDL-TG can also be calculated from the results of the standard lipid panel. Compared to estimated LDL-C, eLDL-TG was a better risk marker for primary prevention and hence could improve initial ASCVD risk stratification.

Objective:. The objective of this study was to measure potential associations between surgeon sex and number of days alive and at home (DAH). Background:. Patients treated by female surgeons appear to have lower rates of mortality, complications, readmissions, and healthcare costs when compared with male surgeons. DAH is a validated measure, shown to better capture the patient experience of postoperative recovery. Methods:. We conducted a retrospective study of adults (≥18 years of age) undergoing common surgeries between January 01, 2007 and December 31, 2019 in Ontario, Canada. The outcome measures were the number of DAH within 30-, 90-, and 365-days. The data was summarized using descriptive statistics and adjusted using multivariable generalized estimating equations. Results:. During the study period, 1,165,711 individuals were included, of which 61.9% (N = 721,575) were female. Those managed by a female surgeon experienced a higher mean number of DAH when compared with male surgeons at 365 days (351.7 vs. 342.1 days; P < 0.001) and at each earlier time point. This remained consistent following adjustment for covariates, with patients of female surgeons experiencing a higher number of DAH at all time points, including at 365 days (343.2 [339.5–347.1] vs. 339.4 [335.9–343.0] days). Multivariable regression modeling revealed that patients of male surgeons had a significantly lower number of DAH versus female surgeons. Conclusions:. Patients of female surgeons experienced a higher number of DAH when compared with those treated by male surgeons at all time points. More time spent at home after surgery may in turn lower costs of care, resource utilization, and potentially improve quality of life. Further studies are needed to examine these findings across other care contexts.

The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test length (10, 30, and 50), number of classes (2 and 3), the degree of latent class separation (normal/no separation, small, medium, and large), and class sizes (equal vs. nonequal). Effects were assessed using root mean square error (RMSE) and classification accuracy percentage computed between true parameters and estimated parameters. The results of this simulation study showed that more precise estimates of item parameters were obtained with larger sample sizes and longer test lengths. Recovery of item parameters decreased as the number of classes increased with the decrease in sample size. Recovery of classification accuracy for the conditions with two-class solutions was also better than that of three-class solutions. Results of both item parameter estimates and classification accuracy differed by model type. More complex models and models with larger class separations produced less accurate results. The effect of the mixture proportions also differentially affected RMSE and classification accuracy results. Groups of equal size produced more precise item parameter estimates, but the reverse was the case for classification accuracy results. Results suggested that dichotomous mixture IRT models required more than 2,000 examinees to be able to obtain stable results as even shorter tests required such large sample sizes for more precise estimates. This number increased as the number of latent classes, the degree of separation, and model complexity increased.

Abstract Multiple Myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells within the bone marrow. Diagnosing MM presents considerable challenges, involving the identification of plasma cells in cytology examinations on hematological slides. At present, this is still a time-consuming manual task and has high labor costs. These challenges have adverse implications, which rely heavily on medical professionals’ expertise and experience. To tackle these challenges, we present an investigation using Artificial Intelligence, specifically a Machine Learning analysis of hematological slides with a Deep Neural Network (DNN), to support specialists during the process of diagnosing MM. In this sense, the contribution of this study is twofold: in addition to the trained model to diagnose MM, we also make available to the community a fully-curated hematological slide dataset with thousands of images of plasma cells. Taken together, the setup we established here is a framework that researchers and hospitals with limited resources can promptly use. Our contributions provide practical results that have been directly applied in the public health system in Brazil. Given the open-source nature of the project, we anticipate it will be used and extended to diagnose other malignancies.

Abstract Background Water quality is known to influence the development and survival of larval mosquitoes, which affects mosquito-borne pathogen transmission as a function of the number of mosquitoes that reach adulthood and blood feed. Although water properties are known to affect mosquito development, few studies have investigated the link among soil properties, water quality, and mosquito development. Given the large number of ground-breeding mosquito species, this linkage is a potentially important factor to consider in mosquito ecology. In this study, we explored the effects of different soils on multiple life history parameters of the ground-breeding mosquito species Culex quinquefasciatus (Diptera: Culicidae). Methods Cx. quinquefasciatus larvae were reared in water combined with different soil substrates (sandy, silt, or clay loam textures) at increasing soil to water volume ratios, with and without the addition of organic matter (fish food). Gravid mosquitoes were offered different soil–water extracts to investigate soil effects on oviposition preference. Results Without the addition of organic matter, larval survival and development differed significantly among waters with different soil textures and volumes of substrate. Mosquitoes in water with clay loam soil survived longer and developed further than mosquitoes in other soil waters. Larvae survived for longer periods of time with increased volumes of soil substrate. Adding organic matter reduced the differences in larval survival time, development, and pupation among soil–water extracts. Adult female mosquitoes oviposited more frequently in water with clay loam soil, but the addition of organic matter reduced the soil effects on oviposition preference. Conclusions This study suggests soil composition affects larval mosquito survival and development, as well as the oviposition preference of gravid females. Future studies could differentiate abiotic and biotic soil features that affect mosquitoes and incorporate soil variation at the landscape scale into models to predict mosquito population dynamics and mosquito-borne pathogen transmission. Graphical Abstract

Objective: Lipoprotein(a) [Lp(a)] has been associated with Atherosclerotic Cardiovascular Disease (ASCVD). Approximately 20 % of the population has elevated Lp(a). Despite its well-recognized role in ASCVD, universal screening remains controversial. The aim of our study is to investigate laboratory testing patterns for Lp(a) in subjects screened with a standard lipid panel at a large tertiary referring US institution. Methods: Data were retrospectively collected at Mayo Clinic from the Mayo Data Explorer (MDE). Subjects were included if they had a lipid panel measured between May 1, 2022, and April 30, 2023. Demographic data, Lp(a) measurements, statins and aspirin prescription and ASCVD events which occurred at any time in the life of a subject were recorded along with respective dates. The cumulative number of Lp(a) laboratory test orders were also tallied from 1994 to 2023 independently of the lipid panel requests. Results: Between May 1, 2022, and April 30, 2023, 257,225 subjects had a lipid panel ordered. Of these, only 386 (0.15 %) had Lp(a) tested within 1 year of the lipid panel, while 2406 (0.94 %) had Lp(a) tested at any time. Lp(a) was tested more frequently in males (67 %) and in subjects who developed Myocardial Infarction (MI) at any time (12 %). Following Lp(a) results, there was no significant change in statin or aspirin prescription associated with Lp(a) levels. Secondary prevention was the main setting for ordering Lp(a) testing, and there was no change in this trend throughout the years. Conclusions: Testing rates for Lp(a) in the general population are low and the main setting remains secondary prevention. Women are less tested than men. When Lp(a) is found to be elevated, often times there is no change in patient management to mitigate the ASCVD risk.

Introduction Clinical assessment in emergency departments (EDs) for possible acute myocardial infarction (AMI) requires at least one cardiac troponin (cTn) blood test. The turn-around time from blood draw to posting results in the clinical portal for central laboratory analysers is ~1–2 hours. New generation, high-sensitivity, point-of-care cardiac troponin I (POC-cTnI) assays use whole blood on a bedside (or near bedside) analyser that provides a rapid (8 min) result. This may expedite clinical decision-making and reduce length of stay. Our purpose is to determine if utilisation of a POC-cTnI testing reduces ED length of stay. We also aim to establish an optimised implementation process for the amended clinical pathway.Methods and analysis This quality improvement initiative has a pragmatic multihospital stepped-wedge cross-sectional cluster randomised design. Consecutive patients presenting to the ED with symptoms suggestive of possible AMI and having a cTn test will be included. Clusters (comprising one or two hospitals each) will change from their usual-care pathway to an amended pathway using POC-cTnI—the ‘intervention’. The dates of change will be randomised. Changes occur at 1 month intervals, with a minimum 2 month ‘run-in’ period. The intervention pathway will use a POC-cTnI measurement as an alternate to the laboratory-based cTn measurement. Clinical decision-making steps and logic will otherwise remain unchanged. The POC-cTnI is the Siemens (Erlangen Germany) Atellica VTLi high-sensitivity cTnI assay. The primary outcome is ED length of stay. The safety outcome is cardiac death or AMI within 30 days for patients discharged directly from the ED.Ethics and dissemination Ethics approval has been granted by the New Zealand Southern Health and Disability Ethics Committee, reference 21/STH/9. Results will be published in a peer-reviewed journal. Lay and academic presentations will be made. Māori-specific results will be disseminated to Māori stakeholders.Trial registration number ACTRN12619001189112.

Terephthalic acid (H2TPA) solubility in several ionic liquids (ILs) at multiple concentrations is higher than for any other known solvents at lower temperatures and pressures which suggests low energy purification of H2TPA from its major impurity, 4-carboxybenzaldehyde (4-CBA) might be possible. To understand the mechanism several strategies were explored to purify H2TPA by taking advantage of this high solubilizing power of ILs for H2TPA in the crystallization of unique salts and cocrystals. Using either zwitterionic carboxylate IL-precursors or direct salt formation with carboxylate ILs or amines, a series of salts of mono, dibasic and two ionic cocrystals were obtained including monobasic, [C1C1im][HTPA], [N4441][HTPA], [C4C1im][HTPA]•0.5H2TPA (a cocrystal), and [C1Him][HTPA] ([C1C1im]+ = 1,3-dimethylimidazolium, [N4441]+ = tribuytlmethylammonium, [C1Him]+ = 1-methyl-3-H-imidazolium), dibasic [C1C1im]2[TPA], [C4C1im]2[TPA], [N4444]2[TPA], [C1Him]2[TPA], [H2N22]2[TPA], [H3N6]2[TPA], and [HN(CH2CH2OH)3]2[TPA] ([C4C1im]+ = 1-butyl-3-methylimidazolium, [N4444]+ = tetrabuylammonium, [H3N6]+ = hexylammonium, [HN(CH2CH2OH)3]+ = triethanolammonium, [H2N22]+= diethylammonium), and a second cocrystal [C2C1im]Cl•0.5H2TPA. The formation of these salts suggest a viable method to purify H2TPA because of preferred salt formation at low energy conditions. One elegant route using 1-ethyl-3-methylimidazolium chloride ([C2C1im]Cl) could be especially promising because the cocrystal [C2C1im]Cl•0.5H2TPA was readily isolated and is easily dissociated when exposed to ambient conditions into crystalline H2TPA and a liquid of hydrated [C2C1im]Cl.

Abstract Background The accurate measurement of Low-density lipoprotein cholesterol (LDL-C) is critical in the decision to utilize the new lipid-lowering therapies like PCSK9-inhibitors (PCSK9i) for high-risk cardiovascular disease patients that do not achieve sufficiently low LDL-C on statin therapy. Objective To improve the estimation of low LDL-C by developing a new equation that includes apolipoprotein B (apoB) as an independent variable, along with the standard lipid panel test results. Methods Using β-quantification (BQ) as the reference method, which was performed on a large dyslipidemic population (N = 24,406), the following enhanced Sampson-NIH equation (eS LDL-C) was developed by least-square regression analysis: $$eS\,LDL-C= \frac{TC}{1.15}-\frac{HDL-C}{1.25}-\frac{TG}{6.99}-\frac{\left(TG\times NonHDL-C\right)}{1120}+\frac{{TG}^{2}}{8910}+\frac{\left(TG\times ApoB\right)}{1240}+\frac{ApoB}{4.54}-4.73$$ e S L D L - C = TC 1.15 - H D L - C 1.25 - TG 6.99 - T G × N o n H D L - C 1120 + TG 2 8910 + T G × A p o B 1240 + ApoB 4.54 - 4.73 Results The eS LDL-C equation was the most accurate equation for a broad range of LDL-C values based on regression related parameters and the mean absolute difference (mg/dL) from the BQ reference method (eS LDL-C: 4.51, Sampson-NIH equation [S LDL-C]: 6.07; extended Martin equation [eM LDL-C]: 6.64; Friedewald equation [F LDL-C]: 8.3). It also had the best area-under-the-curve accuracy score by Regression Error Characteristic plots for LDL-C

Evaluating science learning through written assessments for Multilingual Learners (MLs) can offer a critical source of information for educators aiming to adopt culturally and linguistically sustaining practices. However, without careful planning and design, written assessments for MLs can yield unreliable data. This conceptual article addresses this challenge by first examining the complexities of the written language of science and its impact on student learning. It then explores current literature and presents the Multidimensional Assessment Performance Analysis (MAPA) framework, a multimodal model for analyzing writing answers in multilingual educational contexts. MAPA integrates Systemic Functional Linguistics (SFL) to evaluate language use and Topic Models to allocate students based on their cognitive reasoning and thinking patterns. The article concludes with recommendations for classroom teachers and researchers to enhance assessment practices in science education for MLs.

A 63-year-old woman presented with atypical chest pain after a third dose of the coronavirus disease 2019 (COVID-19) messenger ribonucleic acid (mRNA) vaccine. Serial cardiac troponin measurements were performed to evaluate the trajectory of her time-concentration curve which showed a typical myocarditis curve with rapid normalization. The diagnosis of myocarditis was confirmed by cardiac magnetic resonance imaging and follow-up imaging showed resolution. All symptoms resolved with weeks.

The participation of countries in various international large-scale assessments (ILSA) is motivated by different factors. In recent years however, there has been a growing popularity and importance placed on the results of ILSA. Recognising the significance of ILSA as a valuable source of feedback for enhancing the basic education system, the Philippines, through its Department of Education, has actively engaged in participating. This discussion paper examines the current state of the Philippines in relation to ILSA and underscores the need to incorporate the findings of ILSA into the review of the preservice teacher education program. Although there is limited literature available on how ILSA should inform Philippine pre-service teacher education, existing studies suggest that the current teacher education curriculum falls short in terms of meeting the expectations of ILSA. By framing the discussion within the context of the value of ILSA as an assessment system that can provide feedback for educational improvement, it becomes evident that considering ILSA in the program design of pre-service teacher education is a recognition of its significance, despite its Western origins.