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The Electron Cyclotron Emission (ECE) and correlation ECE diagnostics in TCV have been upgraded during its 2014 shutdown; this paper will provide a brief overview on their updated architecture and capabilities. The ECE system is equipped with two radiometers, each with 24 channels (750 MHz bandwidth) looking at the plasma both from the low and high field side covering the whole vessel. Of particular interest is the possibility of acquiring signals through a vertical line of sight and a dual-axis steerable antenna to study the electron distribution function (EDF) in plasmas with Electron Cyclotron Current Drive (ECCD) or EC Resonant Heating (ECRH). The correlation ECE system has been equipped with a new independent front end connected to the steerable antenna. The main characteristics of the radiometer are the six YIG independently tunable (between 6–18 GHz) 170 MHz bandwidth channels that can be moved on the whole LFS of the vessel. Using correlation analysis techniques it is possible to study very small temperature fluctuations. A brief presentation of some applications where these capabilities have been exploited in past TCV experiments for the study of micro instabilities characteristics is also included.

Abstract Background Myocardial extracellular volume (ECV) is elevated in fibrosis or infiltration and can be quantified by measuring the haematocrit with pre and post contrast T1 at sufficient contrast equilibrium. Equilibrium CMR (EQ-CMR), using a bolus-infusion protocol, has been shown to provide robust measurements of ECV using a multibreath-hold T1 pulse sequence. Newer, faster sequences for T1 mapping promise whole heart coverage and improved clinical utility, but have not been validated. Methods Multibreathhold T1 quantification with heart rate correction and single breath-hold T1 mapping using Shortened Modified Look-Locker Inversion recovery (ShMOLLI) were used in equilibrium contrast CMR to generate ECV values and compared in 3 ways. Firstly, both techniques were compared in a spectrum of disease with variable ECV expansion (n=100, 50 healthy volunteers, 12 patients with hypertrophic cardiomyopathy, 18 with severe aortic stenosis, 20 with amyloid). Secondly, both techniques were correlated to human histological collagen volume fraction (CVF%, n=18, severe aortic stenosis biopsies). Thirdly, an assessment of test:retest reproducibility of the 2 CMR techniques was performed 1 week apart in individuals with widely different ECVs (n=10 healthy volunteers, n=7 amyloid patients). Results More patients were able to perform ShMOLLI than the multibreath-hold technique (6% unable to breath-hold). ECV calculated by multibreath-hold T1 and ShMOLLI showed strong correlation (r2=0.892), little bias (bias -2.2%, 95%CI -8.9% to 4.6%) and good agreement (ICC 0.922, range 0.802 to 0.961, p2= 0.589) but better by ShMOLLI ECV (r2= 0.685). Inter-study reproducibility demonstrated that ShMOLLI ECV trended towards greater reproducibility than the multibreath-hold ECV, although this did not reach statistical significance (95%CI -4.9% to 5.4% versus 95%CI -6.4% to 7.3% respectively, p=0.21). Conclusions ECV quantification by single breath-hold ShMOLLI T1 mapping can measure ECV by EQ-CMR across the spectrum of interstitial expansion. It is procedurally better tolerated, slightly more reproducible and better correlates with histology compared to the older multibreath-hold FLASH techniques.

Abstract The need for a collaborative approach to complex inherited diseases collectively referred to as laminopathies, encouraged Italian researchers, geneticists, physicians and patients to join in the Italian Network for Laminopathies, in 2009. Here, we highlight the advantages and added value of such a multidisciplinary effort to understand pathogenesis, clinical aspects and try to find a cure for Emery-Dreifuss muscular dystrophy, Mandibuloacral dysplasia, Hutchinson-Gilford Progeria and forms of lamin-linked cardiomyopathy, neuropathy and lipodystrophy.

Endurance events are popular worldwide and have many health benefits. However, runners and Para athletes may sustain musculoskeletal injuries or experience other health consequences from endurance events. The American Medical Society for Sports Medicine (AMSSM) Runner Health Consortium aimed to generate consensus-based survey items for use in prospective research to identify risk factors for injuries in runners and Para athletes training and competing in endurance events. The study design employed a modified Delphi approach, with a panel comprising 28 experts, including healthcare professionals, coaches, and athletes. Potential survey items were generated by panel members who subsequently engaged in three rounds of voting using Research Electronic Data Capture. Items were graded by clarity, relevance, and importance. Items achieving 80% consensus on all three aspects were retained. The response rate was 100% in R round 1 and 96% in Rrounds 2 and 3. Of 124 initial survey items, consensus was reached on 53, 34 and 22 items during Rrounds 1, 2, and 3, respectively. Two accepted items were removed due to redundancy. Combined with 10 non-voting items, 117 items covered key domains, including training and injury history, dietary behaviours and associated factors (such as menstrual function), footwear, mental health, and specific considerations for Para athletes. The consensus-based survey items should be considered by researchers to better understand the health of runners and Para athletes who train and compete in endurance sports to identify risk factors for injury., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Rheumatoid arthritis (RA) is a complex immune-mediated inflammatory disorder in which patients suffer from inflammatory-erosive arthritis. Recent advances on histopathology heterogeneity of RA synovial tissue revealed three distinct phenotypes based on cellular composition (pauci-immune, diffuse and lymphoid), suggesting that distinct etiologies warrant specific targeted therapy which motivates a need for cost effective phenotyping tools in preclinical and clinical settings. To this end, we developed an automated multi-scale computational pathotyping (AMSCP) pipeline for both human and mouse synovial tissue with two distinct components that can be leveraged together or independently: (1) segmentation of different tissue types to characterize tissue-level changes, and (2) cell type classification within each tissue compartment that assesses change across disease states. Here, we demonstrate the efficacy, efficiency, and robustness of the AMSCP pipeline as well as the ability to discover novel phenotypes. Taken together, we find AMSCP to be a valuable cost-effective method for both pre-clinical and clinical research., (© 2024. The Author(s).)

Background: When evaluating the results of clinical research studies, readers need to know that patients perceive effect sizes, not p values. Knowing the minimum clinically important difference (MCID) and the patient-acceptable symptom state (PASS) threshold for patient-reported outcome measures helps us to ascertain whether our interventions result in improvements that are large enough for patients to care about, and whether our treatments alleviate patient symptoms sufficiently. Prior studies have developed the MCID and PASS threshold for the Hip Disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS JR) and Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) anchored on satisfaction with surgery, but to our knowledge, neither the MCID nor the PASS thresholds for these instruments anchored on a single-item PASS question have been described., Questions/purposes: (1) What are the MCID (defined here as the HOOS/KOOS JR change score associated with achieving PASS) and PASS threshold for the HOOS JR and KOOS JR anchored on patient responses to the single-item PASS instrument? (2) How do patient demographic factors such as age, gender, and BMI correlate with MCID and PASS thresholds using the single-item PASS instrument?, Methods: Between July 2020 and September 2021, a total of 10,970 patients underwent one primary unilateral THA or TKA and completed at least one of the three surveys (preoperative HOOS or KOOS JR, 1-year postoperative HOOS or KOOS JR, and 1-year postoperative single-item anchor) at one large, academic medical center. Of those, only patients with data for all three surveys were eligible, leaving 13% (1465 total; 783 THAs and 682 TKAs) for analysis. Despite this low percentage, the overall sample size was large, and there was little difference between completers and noncompleters in terms of demographics or baseline patient-reported outcome measure scores. Patients undergoing bilateral total joint arthroplasty or revision total joint arthroplasty and those without all three surveys at 1 year of follow-up were excluded. A receiver operating characteristic curve analysis, leveraging a 1-year, single-item PASS (that is, "Do you consider that your current state is satisfactory?" with possible answers of "yes" or "no") as the anchor was then used to establish the MCID and PASS thresholds among the 783 included patients who underwent primary unilateral THA and 682 patients who underwent primary unilateral TKA. We also explored the associations of age at the time of surgery (younger than 65 years or 65 years and older), gender (men or women), BMI (< 30 or ≥ 30 kg/m 2 ), and baseline Patient-Reported Outcome Measure Information System-10 physical and mental component scores (< 50 or ≥ 50) for each of the MCID and PASS thresholds through stratified analyses., Results: For the HOOS JR, the MCID associated with the PASS was 23 (95% CI 18 to 31), with an area under the receiver operating characteristic curve of 0.75, and the PASS threshold was 81 (95% CI 77 to 85), with an area under the receiver operating characteristic curve of 0.81. For the KOOS JR, the MCID was 16 (95% CI 14 to 18), with an area under the receiver operating characteristic curve of 0.75, and the PASS threshold was 71 (95% CI 66 to 73) with an area under the receiver operating characteristic curve of 0.84. Stratified analyses indicated higher change scores and PASS threshold for younger men undergoing THA and higher PASS thresholds for older women undergoing TKA., Conclusion: Here, we demonstrated the utility of a single patient-centered anchor question, raising the question as to whether simply collecting a postoperative PASS is an easier way to measure success than collecting preoperative and postoperative patient-reported outcome measures and then calculating MCIDs and the substantial clinical benefit., Level of Evidence: Level III, therapeutic study., Competing Interests: Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research ® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2023 by the Association of Bone and Joint Surgeons.)

Objectives: The purpose of this study was to describe injury patterns and healthcare utilisation of marathon runners., Methods: This was a previously reported 16-week prospective observational study of runners training for the New York City Marathon. Runners completed a baseline survey including demographics, running experience and marathon goal. Injury surveys were collected every 4 weeks during training, as well as 1 week before and 1 week after the race. Injury details collected included anatomic location, diagnosis, onset, and treatment received., Results: A total of 1049 runners were enrolled. Injuries were reported by 398 (38.4%) during training and 128 (14.1%) during the marathon. The overall prevalence of injury was 447/1049 (42.6%). Foot, knee and hip injuries were most common during training, whereas knee, thigh and foot injuries were most common during the race. The most frequent tissue type affected was the category of muscle, tendon/fascia and bursa. The prevalence of overuse injuries increased, while acute injuries remained constant throughout training. Hamstring injuries had the highest prevalence of diagnosis with 38/564 injuries (6.7%). Of the 447 runners who reported an injury, 224 (50.1%) received medical care. Physical therapy was the most common medical care received with 115/1037 (11.1%) runners during training and 44/907 (4.9%) postrace., Conclusion: Runners training and participating in a marathon commonly experience injuries, especially of the foot and knee, which often are overuse soft tissue injuries. Half of the injured runners sought out medical care for their injury. Understanding the patterns of injuries affecting marathon runners could help guide future injury prevention efforts., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Far more publications are available for osteoarthritis of the knee than of the hip. Recognizing this research gap, the Arthritis Foundation (AF), in partnership with the Hospital for Special Surgery (HSS), convened an in-person meeting of thought leaders to review the state of the science of and clinical approaches to hip osteoarthritis. This article summarizes the recommendations gleaned from presentations given in the "late-stage osteoarthritis" session of the 2023 Hip Osteoarthritis Clinical Studies Conference, which took place on February 17 and 18, 2023, in New York City. It covers conservative treatment, decision-making in end-stage hip osteoarthritis, advancements in robotics, and the role of phenotyping in precision rehabilitation post-total hip arthroplasty (THA)., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.B., MD, reports relationships with Smith+Nephew, Lima Corporate, and Globus Medical. B.C., MD, reports relationships with Smith+Nephew, Orthodevelopment, HSS Journal, and Journal of Arthroplasty. M.B., PhD, reports relationships with the NIH (clinical trial grant # R01HD084124). M.P.B., MD, reports relationships with Smith+Nephew, Hip Society, and American Austrian Foundation. The other authors declare no potential conflicts of interest., (© The Author(s) 2023.)

Background: Unplanned hospital readmissions after total joint arthroplasty (TJA) represent potentially serious adverse events and remain a critical measure of hospital quality. Predicting the risk of readmission after TJA may provide patients and clinicians with valuable information for preoperative decision-making., Questions/purposes: (1) Can nonlinear machine-learning models integrating preoperatively available patient, surgeon, hospital, and county-level information predict 30-day unplanned hospital readmissions in a large cohort of nationwide Medicare beneficiaries undergoing TJA? (2) Which predictors are the most important in predicting 30-day unplanned hospital readmissions? (3) What specific information regarding population-level associations can we obtain from interpreting partial dependency plots (plots describing, given our modeling choice, the potentially nonlinear shape of associations between predictors and readmissions) of the most important predictors of 30-day readmission?, Methods: National Medicare claims data (chosen because this database represents a large proportion of patients undergoing TJA annually) were analyzed for patients undergoing inpatient TJA between October 2016 and September 2018. A total of 679,041 TJAs (239,391 THAs [61.3% women, 91.9% White, 52.6% between 70 and 79 years old] and 439,650 TKAs [63.3% women, 90% White, 55.2% between 70 and 79 years old]) were included. Model features included demographics, county-level social determinants of health, prior-year (365-day) hospital and surgeon TJA procedure volumes, and clinical classification software-refined diagnosis and procedure categories summarizing each patient's Medicare claims 365 days before TJA. Machine-learning models, namely generalized additive models with pairwise interactions (prediction models consisting of both univariate predictions and pairwise interaction terms that allow for nonlinear effects), were trained and evaluated for predictive performance using area under the receiver operating characteristic (AUROC; 1.0 = perfect discrimination, 0.5 = no better than random chance) and precision-recall curves (AUPRC; equivalent to the average positive predictive value, which does not give credit for guessing "no readmission" when this is true most of the time, interpretable relative to the base rate of readmissions) on two holdout samples. All admissions (except the last 2 months' worth) were collected and split randomly 80%/20%. The training cohort was formed with the random 80% sample, which was downsampled (so it included all readmissions and a random, equal number of nonreadmissions). The random 20% sample served as the first test cohort ("random holdout"). The last 2 months of admissions (originally held aside) served as the second test cohort ("2-month holdout"). Finally, feature importances (the degree to which each variable contributed to the predictions) and partial dependency plots were investigated to answer the second and third research questions., Results: For the random holdout sample, model performance values in terms of AUROC and AUPRC were 0.65 and 0.087, respectively, for THA and 0.66 and 0.077, respectively, for TKA. For the 2-month holdout sample, these numbers were 0.66 and 0.087 and 0.65 and 0.075. Thus, our nonlinear models incorporating a wide variety of preoperative features from Medicare claims data could not well-predict the individual likelihood of readmissions (that is, the models performed poorly and are not appropriate for clinical use). The most predictive features (in terms of mean absolute scores) and their partial dependency graphs still confer information about population-level associations with increased risk of readmission, namely with older patient age, low prior 365-day surgeon and hospital TJA procedure volumes, being a man, patient history of cardiac diagnoses and lack of oncologic diagnoses, and higher county-level rates of hospitalizations for ambulatory-care sensitive conditions. Further inspection of partial dependency plots revealed nonlinear population-level associations specifically for surgeon and hospital procedure volumes. The readmission risk for THA and TKA decreased as surgeons performed more procedures in the prior 365 days, up to approximately 75 TJAs (odds ratio [OR] = 1.2 for TKA and 1.3 for THA), but no further risk reduction was observed for higher annual surgeon procedure volumes. For THA, the readmission risk decreased as hospitals performed more procedures, up to approximately 600 TJAs (OR = 1.2), but no further risk reduction was observed for higher annual hospital procedure volumes., Conclusion: A large dataset of Medicare claims and machine learning were inadequate to provide a clinically useful individual prediction model for 30-day unplanned readmissions after TKA or THA, suggesting that other factors that are not routinely collected in claims databases are needed for predicting readmissions. Nonlinear population-level associations between low surgeon and hospital procedure volumes and increased readmission risk were identified, including specific volume thresholds above which the readmission risk no longer decreases, which may still be indirectly clinically useful in guiding policy as well as patient decision-making when selecting a hospital or surgeon for treatment., Level of Evidence: Level III, therapeutic study., Competing Interests: Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2023 by the Association of Bone and Joint Surgeons.)

Background: As the demand for total hip arthroplasty (THA) rises, a predictive model for THA risk may aid patients and clinicians in augmenting shared decision-making. We aimed to develop and validate a model predicting THA within 10 years in patients using demographic, clinical, and deep learning (DL)-automated radiographic measurements., Methods: Patients enrolled in the osteoarthritis initiative were included. DL algorithms measuring osteoarthritis- and dysplasia-relevant parameters on baseline pelvis radiographs were developed. Demographic, clinical, and radiographic measurement variables were then used to train generalized additive models to predict THA within 10 years from baseline. A total of 4,796 patients were included [9,592 hips; 58% female; 230 THAs (2.4%)]. Model performance using 1) baseline demographic and clinical variables 2) radiographic variables, and 3) all variables was compared., Results: Using 110 demographic and clinical variables, the model had a baseline area under the receiver operating curve (AUROC) of 0.68 and area under the precision recall curve (AUPRC) of 0.08. Using 26 DL-automated hip measurements, the AUROC was 0.77 and AUPRC was 0.22. Combining all variables, the model improved to an AUROC of 0.81 and AUPRC of 0.28. Three of the top five predictive features in the combined model were radiographic variables, including minimum joint space, along with hip pain and analgesic use. Partial dependency plots revealed predictive discontinuities for radiographic measurements consistent with literature thresholds of osteoarthritis progression and hip dysplasia., Conclusion: A machine learning model predicting 10-year THA performed more accurately with DL radiographic measurements. The model weighted predictive variables in concordance with clinical THA pathology assessments., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Achieving adequate implant fixation is critical to optimize survivorship and postoperative outcomes after revision total knee arthroplasty (rTKA). Three anatomical zones (ie, epiphysis, metaphysis, and diaphysis) have been proposed to assess fixation, but are not well-defined. The purpose of the study was to develop a deep learning workflow capable of automatically delineating rTKA zones and cone placements in a standardized way on postoperative radiographs., Methods: A total of 235 patients who underwent rTKA were randomly partitioned (6:2:2 training, validation, and testing split), and a U-Net segmentation workflow was developed to delineate rTKA fixation zones and assess revision cone placement on anteroposterior radiographs. Algorithm performance for zone delineation and cone placement were compared against ground truths from a fellowship-trained arthroplasty surgeon using the dice segmentation coefficient and accuracy metrics., Results: On the testing cohort, the algorithm defined zones in 98% of images (8 seconds/image) using anatomical landmarks. The dice segmentation coefficient between the model and surgeon was 0.89 ± 0.08 (interquartile range [IQR]:0.88-0.94) for femoral zones, 0.91 ± 0.08 (IQR: 0.91-0.95) for tibial zones, and 0.90 ± 0.05 (IQR:0.88-0.94) for all zones. Cone identification and zonal cone placement accuracy were 98% and 96%, respectively, for the femur and 96% and 89%, respectively, for the tibia., Conclusion: A deep learning algorithm was developed to automatically delineate revision zones and cone placements on postoperative rTKA radiographs in an objective, standardized manner. The performance of the algorithm was validated against a trained surgeon, suggesting that the algorithm demonstrated excellent predictive capabilities in accordance with relevant anatomical landmarks used by arthroplasty surgeons in practice., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Limited data exist regarding the association of tibiofemoral bony and soft tissue geometry and knee laxity with risk of first-time noncontact anterior cruciate ligament (ACL) rupture., Purpose: To determine associations of tibiofemoral geometry and anteroposterior (AP) knee laxity with risk of first-time noncontact ACL injury in high school and collegiate athletes., Study Design: Cohort study; Level of evidence, 2., Methods: Over a 4-year period, noncontact ACL injury events were identified as they occurred in 86 high school and collegiate athletes (59 female, 27 male). Sex- and age-matched control participants were selected from the same team. AP laxity of the uninjured knee was measured using a KT-2000 arthrometer. Magnetic resonance imaging was taken on ipsilateral and contralateral knees, and articular geometries were measured. Sex-specific general additive models were implemented to investigate associations between injury risk and 6 features: ACL volume, meniscus-bone wedge angle in the lateral compartment of the tibia, articular cartilage slope at the middle region of the lateral compartment of the tibia, femoral notch width at the anterior outlet, body weight, and AP displacement of the tibia relative to the femur. Importance scores (in percentages) were calculated to rank the relative contribution of each variable., Results: In the female cohort, the 2 features with the highest importance scores were tibial cartilage slope (8.6%) and notch width (8.1%). In the male cohort, the 2 top-ranked features were AP laxity (5.6%) and tibial cartilage slope (4.8%). In female patients, injury risk increased by 25.5% with lateral middle cartilage slope becoming more posteroinferior from -6.2° to -2.0° and by 17.5% with lateral meniscus-bone wedge angle increasing from 27.3° to 28.2°. In males, an increase in AP displacement from 12.5 to 14.4 mm in response to a 133-N anterior-directed load was associated with a 16.7% increase in risk., Conclusion: Of the 6 variables studied, there was no single dominant geometric or laxity risk factor for ACL injury in either the female or male cohort. In males, AP laxity >13 to 14 mm was associated with sharply increased risk of noncontact ACL injury. In females, lateral meniscus-bone wedge angle >28° was associated with a sharply decreased risk of noncontact ACL injury., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: Funding was received from the National Institute of Arthritis and Musculoskeletal and Skin Diseases at the National Institutes of Health (grants R01AR050421 and R21AR073388), Gosnell Family, Steers Family, Ludwig Family, Clark Foundation, and Kirby Foundation. D.H.N. has received education payments from Arthrex, consulting fees from Linvatec and Newclip, and hospitality payments from Stryker. T.L.W. has received royalties from Stryker. A.D.P. has received research support from Stryker; consulting fees from DePuy, Exactech, Smith & Nephew, and Stryker; nonconsulting fees from Smith & Nephew; and royalties from Zimmer Biomet. A.D.P. also has stock/stock options in Engage. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto., (© The Author(s) 2023.)

Objective: Training patterns are commonly implicated in running injuries. The purpose of this study was to measure the incidence of injury and illness among marathon runners and the association of injuries with training patterns and workload., Methods: Runners registered for the New York City Marathon were eligible to enrol and prospectively monitored during the 16 weeks before the marathon, divided into 4-week 'training quarters' (TQ) numbered TQ1-TQ4. Training runs were tracked using Strava, a web and mobile platform for tracking exercise. Runners were surveyed at the end of each TQ on injury and illness, and to verify all training runs were recorded. Acute:chronic workload ratio (ACWR) was calculated by dividing the running distance in the past 7 days by the running distance in the past 28 days and analysed using ratio thresholds of 1.3 and 1.5., Results: A total of 735 runners participated, mean age 41.0 (SD 10.7) and 46.0% female. Runners tracked 49 195 training runs. The incidence of injury during training was 40.0% (294/735), and the incidence of injury during or immediately after the marathon was 16.0% (112/699). The incidence of illness during training was 27.2% (200/735). Those reporting an initial injury during TQ3 averaged less distance/week during TQ2 compared with uninjured runners, 27.7 vs 31.9 miles/week (p=0.018). Runners reporting an initial injury during TQ1 had more days when the ACWR during TQ1 was ≥1.5 compared with uninjured runners (injured IQR (0-3) days vs uninjured (0-1) days, p=0.009). Multivariable logistic regression for training injuries found an association with the number of days when the ACWR was ≥1.5 (OR 1.06, 95% CI (1.02 to 1.10), p=0.002)., Conclusion: Increases in training volume ≥1.5 ACWR were associated with more injuries among runners training for a marathon. These findings can inform training recommendations and injury prevention programmes for distance runners., Competing Interests: Competing interests: BGT is an associated editor for BJSM., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest form is provided with the online version of the article (http://links.lww.com/JBJS/G1000).

Objectives: To determine how baseline characteristics of first-time marathon runners and training patterns are associated with risk of injuries during training and the race., Methods: First-time adult marathon runners who were registered for the 2017 New York City Marathon were monitored starting 12 weeks prior to the race. Baseline data collection included demographics and running experience. Running frequency, distance, and injury occurrence were self-reported using online surveys every 2 weeks., Results: A total of 720 runners participated of which 675 completed the study. There were 64/675 (9.5%) who had major injuries during training or the race that preventing starting or finishing the race. An additional 332 (49.2%) had minor injuries interfering with training and/or affecting race performance. Injury incidence was not significantly different based on age or sex. Runners who completed a half marathon prior to the study were less likely to report getting injured [multivariable odds ratio (OR) 0.40, (0.22, 0.76), p = 0.005]. Runners who averaged <4 training runs per week during the study were less likely to report getting injured compared to those who averaged ≥4 per week [relative risk 1.36, (1.13-1.63), p = 0.001]. Longest training run distance during the study was inversely associated with race-day injury incidence [OR 0.87 (0.81, 0.94), p < 0.001]., Conclusion: Injuries are common among first-time marathon runners. We found that risk of injury during training was associated with lack of half marathon experience and averaging ≥4 training runs per week. Longer training runs were associated with a lower incidence of race-day injuries. These results can inform the development of targeted injury-prevention interventions.

Background: Coronavirus disease 2019 (COVID-19) affects multiple organ systems. Whether and how COVID-19 affects the musculoskeletal system remains unknown. We aim to assess the association between COVID-19 and risk of injury., Hypothesis: Runners who report having COVID-19 also report a higher incidence of injury., Study Design: Cross-sectional study., Level of Evidence: Level 4., Methods: An electronic survey was distributed from July through September 2020, by New York Road Runners, ASICS North America, race medical directors, and through social media. Inclusion criteria were runners 18 years or older who had participated in ≥1 race (running or triathlon) in 2019., Results: A total of 1947 runners participated and met inclusion criteria. Average age was 45.0 (SD, 12.2) years and 56.5% were women. A total of 123 (6.3%) runners self-reported having COVID-19; 100 (81%) reported their diagnosis was from a laboratory test (polymerase chain reaction or antibody) and 23 reported being diagnosed by a medical professional without confirmatory laboratory testing. Since March 2020, 427 (21.9%) reported an injury that prevented running for at least 1 week, including 38 of 123 (30.9%) who self-reported having COVID-19 and 389 of 1435 (21.3%) who did not report having COVID-19 ( P = 0.01). After adjusting for age, sex, the number of races in 2019, and running patterns before March 2020, runners who self-reported a diagnosis of COVID-19 had a higher incidence of injury compared with those who did not (odds ratio, 1.66; 95% CI, 1.11-2.48; P = 0.01)., Conclusion: Injuries were more often self-reported by runners with laboratory-confirmed or clinically diagnosed COVID-19 compared with those who did not report COVID-19. Given the limitations of the study, any direct role of COVID-19 in the pathophysiology of injuries among runners remains unclear., Clinical Relevance: Direct and indirect musculoskeletal sequelae of COVID-19 should be further investigated, including the risk of exercise- and sports-related injury after COVID-19.

Background Context: Health can impact work performance through absenteeism, time spent away from work, and presenteeism, inhibited at-work performance. Low back pain is common and costly, both in terms of direct medical expenditures and indirect reduced work performance., Purpose: Surgery for lumbar spinal pathology is an important part of treatment for patients who do not respond to nonsurgical management. While the indirect costs of return to work and absenteeism among employed patients undergoing lumbar spine surgery have been studied, little work has been done to quantify presenteeism before and after lumbar spine surgery., Study Design/setting: Prospective cohort study at a single high-volume urban musculoskeletal specialty hospital., Patient Sample: Patients undergoing single-level lumbar spinal fusion and/or decompression surgery., Outcome Measures: Presenteeism and absenteeism were measured using the World Health Organization's Health and Work Performance Questionnaire before surgery, as well as 6 weeks, 6 months, and 12 months after surgery., Methods: Average presenteeism and absenteeism were evaluated at pre-surgical baseline and each follow-up timepoint. Monthly average time lost to presenteeism and absenteeism were calculated before surgery and 12 months after surgery. Study data were collected and managed using REDCap electronic data capture tools with support from Clinical and Translational Science Center grant, UL1TR002384. One author discloses royalties, private investments, consulting fees, speaking/teaching arrangements, travel, board of directorship, and scientific advisory board membership totaling >$300,000., Results: We enrolled 134 employed surgical patients, among whom 115 (86%) responded at 6 weeks, 105 (78%) responded at 6 months, and 115 (86%) responded at 12 months. Preoperatively, mean age was 56.4 years (median 57.5), and 41.0% were women; 68 (50.7%) had only decompressions, while 66 (49.3%) had fusions. Among respondents at each time point, 98%, 92%, and 92% were still employed, among whom 76%, 96%, and 96% had resumed working, respectively (median 29 days). Average at-work performance among working patients (who responded at each pair of timepoints) moved from 75.4 to 78.7 between baseline and 6 weeks, 71.8 to 85.9 between baseline and 6 months, and 73.0 to 88.1 between baseline and 12 months. Gains were concentrated among the 52.0% of patients whose at-work performance was declining (and low) leading up to surgery. Average absenteeism was relatively unmoved between baseline and each follow-up. Before surgery, the monthly average time lost to presenteeism and absenteeism was 19.8% and 18.9%, respectively; 12 months after surgery, these numbers were 9.7% and 16.0%; changes represent a mitigated loss of 13.0 percentage points of average monthly value., Conclusions: Presenteeism and absenteeism contributed roughly evenly to preoperative average monthly lost time. Although average changes in absenteeism and 6-week at-work performance were small, average changes in at-work performance at 6 and 12 months were significant. Cost-benefit analyses of lumbar spine surgery should therefore consider improved presenteeism, which appears to offset some of the direct and indirect costs of surgical treatment., Competing Interests: Declarations of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Decisions regarding care for osteoarthritis involve physicians helping patients understand likely benefits and harms of treatment. Little work has directly compared patient and surgeon risk-taking attitudes, which may help inform strategies for shared decision-making and improve patient satisfaction., Methods: We surveyed patients contemplating total joint arthroplasty visiting a high-volume specialty hospital regarding general questions about risk-taking, as well as willingness to undergo surgery under hypothetical likelihoods of moderate improvement and complications. We compared responses from surgeons answering similar questions about willingness to recommend surgery., Results: Altogether 82% (162/197) of patients responded, as did 65% (30/46) of joint replacement surgeons. Mean age among patients was 66.4 years; 58% were female. Surgeons averaged 399 surgeries in 2019. Responses were similar between groups for general, health, career, financial, and sports/leisure risk-taking (P > .20); surgeons were marginally more risk-taking in driving (P = .05). For willingness to have or recommend surgery, as the chance of benefit decreased, or the chance of harm increased, the percentage willing to have or recommend surgery decreased. Between a 70% and 95% chance of moderate improvement (for a 2% complication risk), as well as between a 90% and 95% chance of moderate improvement (for 4% and 6% complication risks), the percentage willing to have or recommend surgery was indistinguishable between patients and surgeons. However, for lower likelihoods of improvement, a higher percentage of patients were willing to undergo surgery than surgeons recommended. Patients were also more often indifferent between complication risks., Conclusion: Although patients and surgeons were often willing to have or recommend joint replacement surgery at similar rates, they diverged for lower-benefit higher-harm scenarios., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems., (© 2022. The Author(s).)

Background: It is essential to quantify an acceptable outcome after total joint arthroplasty (TJA) in order to understand quality of care. The purpose of this study was to define patient acceptable symptom state (PASS) thresholds for the Knee injury and Osteoarthritis Outcome Score, Joint Replacement (KOOS JR) and the Hip disability and Osteoarthritis Outcome Score, Joint Replacement (HOOS JR) after TJA., Methods: A receiver operating characteristic (ROC) curve analysis, leveraging 2-year satisfaction of "moderate improvement" or better as the anchor, was used to establish PASS thresholds among 5,216 patients who underwent primary total hip arthroplasty and 4,036 who underwent primary total knee arthroplasty from 2007 to 2012 with use of an institutional registry. Changes in PASS thresholds were explored by stratifying and recalculating these thresholds by age at the time of surgery (<70 or ≥70 years of age), sex (men or women), body mass index (BMI; <30 or ≥30 kg/m2), and baseline Short Form-36 (SF-36) physical and mental component scores (<50 or ≥50)., Results: The HOOS JR PASS threshold was 76.7 (area under the ROC curve [AUC] = 0.91), which was achieved by 4,334 patients (83.1%). The KOOS JR PASS threshold was 63.7 (AUC = 0.89), which was achieved by 3,461 patients (85.8%). Covariate stratification demonstrated that PASS thresholds were higher in men compared with women, and in those with higher preoperative SF-36 physical and mental scores (≥50) compared with lower SF-36 scores (<50). Results differed between instruments for BMI and age: higher BMI was associated with a lower PASS threshold for the HOOS JR but a higher PASS threshold for the KOOS JR. The HOOS JR PASS threshold was higher in patients who were <70 years of age compared with those who were ≥70 years of age, but was equivalent for the KOOS JR., Conclusions: The PASS thresholds for the HOOS JR and KOOS JR at 2 years after TJA were 76.7 and 63.7, respectively. The PASS thresholds were associated with certain preoperative covariates, suggesting that an acceptable symptom state after TJA is influenced by patient-specific factors., Level of Evidence: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence., Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/G766)., (Copyright © 2021 by The Journal of Bone and Joint Surgery, Incorporated.)

Background: The impact of previous SARS-CoV-2 infection on the morbidity of elective total joint arthroplasty (TJA) is not fully understood. This study reports on the association between previous COVID-19 disease, hospital length of stay (LOS), and in-hospital complications after elective primary TJA., Methods: Demographics, comorbidities, LOS, and in-hospital complications of consecutive 340 patients with a history of COVID-19 were compared with those of 5014 patients without a history of COVID-19 undergoing TJA. History of COVID-19 was defined as a positive IgG antibody test for SARS-CoV-2 before surgery. All patients were given both antibody and polymerase chain reaction tests before surgery., Results: Patients with a history of COVID-19 were more likely to be obese (43.8% vs 32.4%, P < .001), Black (15.6% vs 6.8%, P < .001), or Hispanic (8.5% vs 5.4%, P  = .028) than patients without a history of COVID-19. COVID-19 treatment was reported by 6.8% of patients with a history of COVID-19. Patients with a history of COVID-19 did not have a significantly longer median LOS after controlling for other factors (for hip replacements, median 2.9 h longer, 95% confidence interval = -2.0 to 7.8, P  = .240; for knee replacements, median 4.1 h longer, 95% confidence interval = -2.4 to 10.5, P  = .214), but a higher percentage were discharged to a post-acute care facility (4.7% vs 1.9%, P  = .001). There was no significant difference in in-hospital complication rates between the 2 groups (0/340 = 0.0% vs 22/5014 = 0.44%, P  = .221)., Conclusions: We do not find differences in LOS or in-hospital complications between the 2 groups. However, more work is needed to confirm these findings, particularly for patients with a history of more severe COVID-19., Level of Evidence: II., (© 2022 Published by Elsevier Inc. on behalf of The American Association of Hip and Knee Surgeons.)

Objectives: To survey runners and triathletes about their willingness to resume in-person racing during the COVID-19 pandemic, health concerns related to mass races and changes in running patterns since the start of the pandemic., Design: An electronic survey was distributed from 15 July to 1 September 2020 to runners and triathletes by New York Road Runners, ASICS North America, and race medical directors, and through social media., Participants: Runners and triathletes 18 years of age or older who participated in at least one race in 2019., Results: A total of 2278 surveys were received. Not all participants answered every question; the denominator represents the number of responses to each question. Most participants were from the USA (1620/1940, 83.5%), of which over half were from New York (812/1475, 55.1%). Regarding when respondents would feel comfortable returning to in-person racing, the most frequent response was 'Whenever local laws allow, but only if there are sufficient precautions' (954/2173, 43.9%), followed by 'Not until there is a vaccine' (540/2173, 24.9%). The most common concerns about in-person races were crowded starting corrals (1802/2084, 86.5%), the number of COVID-19 cases in the race location (1585/2084, 76.1%) and the number of participants (1517/2084, 72.8%). Comparing running patterns before the pandemic to Summer 2020, the mean weekly mileage decreased from 25.5 (SD 15.4) miles to 22.7 (16.2) miles (p<0.001)., Conclusion: Most runners are willing to return to racing when local laws allow, though as of Summer 2020, many desired certain precautions to feel comfortable., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Abstract: With the increasing availability of large clinical registries and administrative data sets, observational (i.e., nonexperimental) orthopaedic research is being performed with increased frequency. While this research substantially advances our field, there are fundamental limitations to what can be determined through a single observational study. Avoiding overstatements and misstatements is important for the sake of accuracy, particularly for ensuring that clinical care is not inadvertently swayed by how an observational study is written up and described. We have noticed that causal language is frequently misused in observational orthopaedic research-that is, language that says or implies that 1 variable definitively causes another, despite the fact that causation can generally only be determined with randomization. In this data-backed commentary, we examine the prevalence of causal language in a random sample of 400 observational orthopaedic studies; we found that causal language was misused in 60% of them. We discuss the implications of these results and how to report observational findings more accurately: the word "association" (and its derivatives) can almost always replace or reframe a causal phrase., Competing Interests: Disclosure: The authors indicated that no external funding was for any aspect of this work. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/G479)., (Copyright © 2021 by The Journal of Bone and Joint Surgery, Incorporated.)

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10 -8 ), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10 -5 ) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (r g ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|r g | ≈ 0.1-0.3) and positive genetic correlations with physical activity (r g ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (r g ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction., (© 2020. The Author(s).)

Background: Absenteeism is costly, yet evidence suggests that presenteeism-illness-related reduced productivity at work-is costlier. We quantified employed patients' presenteeism and absenteeism before and after total joint arthroplasty (TJA)., Methods: We measured presenteeism (0-100 scale, 100 full performance) and absenteeism using the World Health Organization's Health and Work Performance Questionnaire before and after TJA among a convenience sample of employed patients. We captured detailed information about employment and job characteristics and evaluated how and among whom presenteeism and absenteeism improved., Results: In total, 636 primary, unilateral TJA patients responded to an enrollment email, confirmed employment, and completed a preoperative survey (mean age: 62.1 years, 55.3% women). Full at-work performance was reported by 19.7%. Among 520 (81.8%) who responded to a 1-year follow-up, 473 (91.0%) were still employed, and 461 (88.7%) had resumed working. Among patients reporting at baseline and 1 year, average at-work performance improved from 80.7 to 89.4. A Wilcoxon signed-rank test indicated that postoperative performance was significantly higher than preoperative performance (P < .0001). The percentage of patients who reported full at-work performance increased from 20.9% to 36.8% (delta = 15.9%, 95% confidence interval = [10.0%, 21.9%], P < .0001). Presenteeism gains were concentrated among patients who reported declining work performance leading up to surgery. Average changes in absences were relatively small. Combined, the average monthly value lost by employers to presenteeism declined from 15.3% to 8.3% and to absenteeism from 16.9% to 15.5% (ie, mitigated loss of 8.4% of monthly value)., Conclusion: Among employed patients before TJA, presenteeism and absenteeism were similarly costly. After, employed patients reported increased performance, concentrated among those with declining performance leading up to surgery., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objective: There is emerging evidence that COVID-19 disproportionately affects people from racial/ethnic minority and low socioeconomic status (SES) groups. Many physicians across the globe are changing practice patterns in response to the COVID-19 pandemic. We sought to examine the practice changes among rheumatologists and what they perceive the impact to be on their most vulnerable patients., Methods: We administered an online survey to a convenience sample of rheumatologists worldwide during the initial height of the pandemic (between 8 April and 4 May 2020) via social media and group emails. We surveyed rheumatologists about their opinions regarding patients from low SES and racial/ethnic minority groups in the context of the COVID-19 pandemic. Mainly, what their specific concerns were, including the challenges of medication access; and about specific social factors (health literacy, poverty, food insecurity, access to telehealth video) that may be complicating the management of rheumatologic conditions during this time., Results: 548 rheumatologists responded from 64 countries and shared concerns of food insecurity, low health literacy, poverty and factors that preclude social distancing such as working and dense housing conditions among their patients. Although 82% of rheumatologists had switched to telehealth video, 17% of respondents estimated that about a quarter of their patients did not have access to telehealth video, especially those from below the poverty line. The majority of respondents believed these vulnerable patients, from racial/ethnic minorities and from low SES groups, would do worse, in terms of morbidity and mortality, during the pandemic., Conclusion: In this sample of rheumatologists from 64 countries, there is a clear shift in practice to telehealth video consultations and widespread concern for socially and economically vulnerable patients with rheumatic disease., Competing Interests: Competing interests: SI receives grant funds from the National Institute of Arthritis and Musculoskeletal and Skin Diseases; SMG grants and personal fees from Novartis—consulting/research support, Pfizer—consulting/research support, BMC Musculoskeletal Disorders—editorial board and Horizon—research support; DJ-K owns stocks in the following companies: Cytodyn, Walgreens, AstraZeneca. All other authors have declared that no competing interests exist., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Identifying patients at risk of not achieving meaningful gains in long-term postsurgical patient-reported outcome measures (PROMs) is important for improving patient monitoring and facilitating presurgical decision support. Machine learning may help automatically select and weigh many predictors to create models that maximize predictive power. However, these techniques are underused among studies of total joint arthroplasty (TJA) patients, particularly those exploring changes in postsurgical PROMs. QUESTION/PURPOSES: (1) To evaluate whether machine learning algorithms, applied to hospital registry data, could predict patients who would not achieve a minimally clinically important difference (MCID) in four PROMs 2 years after TJA; (2) to explore how predictive ability changes as more information is included in modeling; and (3) to identify which variables drive the predictive power of these models., Methods: Data from a single, high-volume institution's TJA registry were used for this study. We identified 7239 hip and 6480 knee TJAs between 2007 and 2012, which, for at least one PROM, patients had completed both baseline and 2-year followup surveys (among 19,187 TJAs in our registry and 43,313 total TJAs). In all, 12,203 registry TJAs had valid SF-36 physical component scores (PCS) and mental component scores (MCS) at baseline and 2 years; 7085 and 6205 had valid Hip and Knee Disability and Osteoarthritis Outcome Scores for joint replacement (HOOS JR and KOOS JR scores), respectively. Supervised machine learning refers to a class of algorithms that links a mapping of inputs to an output based on many input-output examples. We trained three of the most popular such algorithms (logistic least absolute shrinkage and selection operator (LASSO), random forest, and linear support vector machine) to predict 2-year postsurgical MCIDs. We incrementally considered predictors available at four time points: (1) before the decision to have surgery, (2) before surgery, (3) before discharge, and (4) immediately after discharge. We evaluated the performance of each model using area under the receiver operating characteristic (AUROC) statistics on a validation sample composed of a random 20% subsample of TJAs excluded from modeling. We also considered abbreviated models that only used baseline PROMs and procedure as predictors (to isolate their predictive power). We further directly evaluated which variables were ranked by each model as most predictive of 2-year MCIDs., Results: The three machine learning algorithms performed in the poor-to-good range for predicting 2-year MCIDs, with AUROCs ranging from 0.60 to 0.89. They performed virtually identically for a given PROM and time point. AUROCs for the logistic LASSO models for predicting SF-36 PCS 2-year MCIDs at the four time points were: 0.69, 0.78, 0.78, and 0.78, respectively; for SF-36 MCS 2-year MCIDs, AUROCs were: 0.63, 0.89, 0.89, and 0.88; for HOOS JR 2-year MCIDs: 0.67, 0.78, 0.77, and 0.77; for KOOS JR 2-year MCIDs: 0.61, 0.75, 0.75, and 0.75. Before-surgery models performed in the fair-to-good range and consistently ranked the associated baseline PROM as among the most important predictors. Abbreviated LASSO models performed worse than the full before-surgery models, though they retained much of the predictive power of the full before-surgery models., Conclusions: Machine learning has the potential to improve clinical decision-making and patient care by helping to prioritize resources for postsurgical monitoring and informing presurgical discussions of likely outcomes of TJA. Applied to presurgical registry data, such models can predict, with fair-to-good ability, 2-year postsurgical MCIDs. Although we report all parameters of our best-performing models, they cannot simply be applied off-the-shelf without proper testing. Our analyses indicate that machine learning holds much promise for predicting orthopaedic outcomes.  LEVEL OF EVIDENCE: Level III, diagnostic study.

Background: Volume-outcome relationships are well established for coronary artery bypass grafting and total joint arthroplasty surgery. Although the U.S. Centers for Medicare & Medicaid Services (CMS) Overall Hospital Quality Star Ratings program includes outcome quality measures for these procedures, these outcome quality measures are not counted toward the star ratings for low-volume hospitals. We sought to assess whether excluding low-volume hospitals from surgical quality measures with known volume-outcome relationships affects the star ratings., Methods: We identified quality measures used in CMS's star ratings that are related to surgical procedures with a known volume-outcome relationship and tested for the presence of the volume-outcome association for each of these measures. We then imputed missing values for low-volume hospitals for each measure and otherwise identically repeated the CMS calculations in order to assess the percentages of hospitals with the same, better, or worse ratings., Results: Among the measures used to calculate star ratings, we identified 4 quality measures (2 related to coronary artery bypass grafting and 2 related to total joint arthroplasty) with known volume-outcome relationships that were excluded from the calculations of the star ratings for low-volume hospitals. We confirmed a volume-outcome association in the CMS data for all 4 measures. When total joint arthroplasty complications were imputed for low-volume hospitals and then included in the calculation of the star ratings, over one-third of hospitals received a different rating; both low-volume and other hospitals were more often hurt than helped. Imputing the other 3 quality measures among low-volume hospitals left the ratings unchanged., Conclusions: The CMS star ratings do not fully represent the risks of undergoing procedures at low-volume hospitals, potentially misrepresent quality across facilities, and hence are of uncertain utility to consumers.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.