Temporal trends in incidence of time-loss injuries in four male professional North American sports over 13 seasons

Sports-related injuries increase healthcare cost burden, and in some instances have harmful long term physical and psychological implications. There is currently a lack of comprehensive data on temporal injury trends across professional North American sports. The purpose of this study was to compare temporal trends, according to incidence and time-loss injuries, by body part in professional baseball, basketball, football, and ice hockey. Public injury data from Major League Baseball, National Basketball Association, National Football League, and National Hockey League from 2007 to December 2019 were extracted and used. A mean of 62.49 injuries per 100 players per season was recorded for all professional sports. The groin/hip/thigh reported the greatest season proportional injury incidence for baseball, football, and ice hockey, with the groin/hip/thigh as the third highest injury incidence in basketball. When stratifying by more specific body part groupings, the knee demonstrated the greatest injury proportional incidence for basketball, football, and ice hockey, with the knee as the third highest proportional injury incidence for baseball. There was an increased in basketball ankle injuries following 2011-2012 season. Football and ice hockey reported the greatest concussion proportion incidence, with football demonstrating an increase in concussions over time, and a substantial increase in concussions from the 2014 to 2015 season. These publicly extracted data and findings can be used as a shared resource for professional baseball, basketball, football, and ice hockey for future individual and across sport collaborations concerning resource allocation and decision making in order to improve player health

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review

Background and Objectives When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model. Methods We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020. Results In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%). Conclusion Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review

Background Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome. Methods We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size. Results A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63–82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A similar number of studies did not meet the ≥ 10EPV criteria (75%, 95% CI: 66–84%). The median deficit of the number of events used to develop a model was 75 [IQR: 234 lower to 7 higher]) which reduced to 63 if the total available data (before any data splitting) was used [IQR:225 lower to 7 higher]. Studies that met the minimum required sample size had a median c-statistic of 0.84 (IQR:0.80 to 0.9) and studies where the minimum sample size was not met had a median c-statistic of 0.83 (IQR: 0.75 to 0.9). Studies that met the ≥ 10 EPP criteria had a median c-statistic of 0.80 (IQR: 0.73 to 0.84). Conclusions Prediction models are often developed with no sample size calculation, as a consequence many are too small to precisely estimate the overall risk. We encourage researchers to justify, perform and report sample size calculations when developing a prediction model

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning

Objective: To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology. Study design and setting: We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices. Results: We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs. Conclusion: The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology

Return to performance following severe ankle, knee, and hip injuries in National Basketball Association players

Abstract The purpose of this study was to compare basketball performance markers one year prior to initial severe lower extremity injury, including ankle, knee, and hip injuries, to one- and two-years following injury during the regular NBA season. Publicly available data were extracted through a reproducible extraction computed programmed process. Eligible participants were NBA players with at least three seasons played between 2008 and 2019, with a time-loss injury reported during the study period. Basketball performance was evaluated for season minutes, points, and rebounds. Prevalence of return to performance and linear regressions were calculated. 285 athletes sustained a severe lower extremity injury. 196 (69%) played one year and 130 (45%) played two years following the injury. Time to return to sport was similar between groin/hip/thigh [227 (88)], knee [260 (160)], or ankle [260 (77)] (P = 0.289). 58 (30%) players participated in a similar number of games and 57 (29%) scored similar points one year following injury. 48 (37%) participated in a similar number of games and 55 (42%) scored a similar number of points two years following injury. Less than half of basketball players that suffered a severe lower extremity injury were participating at the NBA level two years following injury, with similar findings for groin/hip/thigh, knee, and ankle injuries. Less than half of players were performing at previous pre-injury levels two years following injury. Suffering a severe lower extremity injury may be a prognostic factor that can assist sports medicine professionals to educate and set performance expectations for NBA players

Reporting guidelines used varying methodology to develop recommendations

Background and Objectives We investigated the developing methods of reporting guidelines in the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network's database. Methods In October 2018, we screened all records and excluded those not describing reporting guidelines from further investigation. Twelve researchers performed duplicate data extraction on bibliometrics, scope, development methods, presentation, and dissemination of all publications. Descriptive statistics were used to summarize the findings. Results Of the 405 screened records, 262 described a reporting guidelines development. The number of reporting guidelines increased over the past 3 decades, from 5 in the 1990s and 63 in the 2000s to 157 in the 2010s. Development groups included 2–151 people. Literature appraisal was performed during the development of 56% of the reporting guidelines; 33% used surveys to gather external opinion on items to report; and 42% piloted or sought external feedback on their recommendations. Examples of good reporting for all reporting items were presented in 30% of the reporting guidelines. Eighteen percent of the reviewed publications included some level of spin. Conclusion Reporting guidelines have been developed with varying methodology. Reporting guideline developers should use existing guidance and take an evidence-based approach, rather than base their recommendations on expert opinion of limited groups of individuals

Anterior Cruciate Ligament Return to Sport after Injury Scale (ACL-RSI) Scores over Time After Anterior Cruciate Ligament Reconstruction: A Systematic Review with Meta-analysis

Background: Psychological readiness is an important consideration for athletes and clinicians when making return to sport decisions following anterior cruciate ligament reconstruction (ACLR). To improve our understanding of the extent of deficits in psychological readiness, a systematic review is necessary. Objective: To investigate psychological readiness (measured via the Anterior Cruciate Ligament-Return to Sport after Injury scale (ACL-RSI)) over time after ACL tear and understand if time between injury and surgery, age, and sex are associated with ACL-RSI scores. Methods: Seven databases were searched from the earliest date available to March 22, 2022. Articles reporting ACL-RSI scores after ACL tear were included. Risk of bias was assessed using the ROBINS-I, RoB-2, and RoBANS tools based on the study design. Evidence certainty was assessed for each analysis. Random-effects meta-analyses pooled ACL-RSI scores, stratified by time post-injury and based on treatment approach (i.e., early ACLR, delayed ACLR, and unclear approach). Results: A total of 83 studies were included in this review (78% high risk of bias). Evidence certainty was ‘weak’ or ‘limited’ for all analyses. Overall, ACL-RSI scores were higher at 3 to 6 months post-ACLR (mean = 61.5 [95% confidence interval (CI) 58.6, 64.4], I2 = 94%) compared to pre-ACLR (mean = 44.4 [95% CI 38.2, 50.7], I2 = 98%), remained relatively stable, until they reached the highest point 2 to 5 years after ACLR (mean = 70.7 [95% CI 63.0, 78.5], I2 = 98%). Meta-regression suggests shorter time from injury to surgery, male sex, and older age were associated with higher ACL-RSI scores only 3 to 6 months post-ACLR (heterogeneity explained R2 = 47.6%), and this reduced 1–2 years after ACLR (heterogeneity explained R2 = 27.0%). Conclusion: Psychological readiness to return to sport appears to improve early after ACL injury, with little subsequent improvement until ≥ 2-years after ACLR. Longer time from injury to surgery, female sex and older age might be negatively related to ACL-RSI scores 12–24 months after ACLR. Due to the weak evidence quality rating and the considerable importance of psychological readiness for long-term outcomes after ACL injury, there is an urgent need for well-designed studies that maximize internal validity and identify additional prognostic factors for psychological readiness at times critical for return to sport decisions. Registration: Open Science Framework (OSF), https://osf.io/2tezs/

The role of luminous substructure in the gravitational lens system MG 2016+112

MG 2016+112 is a quadruply imaged lens system with two complete images A and B and a pair of merging partial images in region C as seen in the radio. The merging images are found to violate the expected mirror symmetry. This indicates an astrometric anomaly which could only be of gravitational origin and could arise due to substructure in the environment or line-of-sight of the lens galaxy. We present new high resolution multi-frequency VLBI observations at 1.7, 5 and 8.4 GHz. Three new components are detected in the new VLBI imaging of both the lensed images A and B. The expected opposite parity of the lensed images A and B was confirmed due to the detection of non-collinear components. Furthermore, the observed properties of the newly detected components are inconsistent with the predictions of previous mass models. We present new scenarios for the background quasar which are consistent with the new observations. We also investigate the role of the satellite galaxy situated at the same redshift as the main lensing galaxy. Our new mass models demonstrate quantitatively that the satellite galaxy is the primary cause of the astrometric anomaly found in region C. The detected satellite is consistent with the abundance of subhaloes expected in the halo from cold dark matter (CDM) simulations. However, the fraction of the total halo mass in the satellite as computed from lens modeling is found to be higher than that predicted by CDM simulations.Comment: 17 pages, 11 figures, accepted with minor revisions in the text including corrected sign of the shear and improved figure

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

Objectives In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. Study Design and Setting We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. Results We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. Conclusion The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence