Examining the Influence of Sex on the Risk of Future Musculoskeletal Injury Following Sports-Related Concussion
Bibliographic record
Abstract
Background: Several studies have demonstrated that athletes are at a significantly increased risk of lower-extremity musculoskeletal injury following return to sport from a concussion. However, the risk of musculoskeletal injury and potential sex differences have not been adequately examined within the interuniversity athlete population. Objective: To determine if interuniversity athletes are at an increased risk of musculoskeletal injury following concussion and if there are any sex differences regarding this risk. Methods: A retrospective cohort study was completed involving 392 cases of 346 interuniversity athletes at the University of Toronto between 2015-16 to 2019-20. Athletes who suffered a concussion during this period (CONC group, n = 98) were matched, with replacement, to an athlete who suffered an index musculoskeletal injury (MSI group, n = 98) and to two healthy control athletes (CTL group, n = 196). Participant athletes’ musculoskeletal injury history for one year after returning to play were extracted from patient medical records. Generalized linear models using a Bernoulli distribution were developed to determine the difference in risk of future musculoskeletal injury between the three groups. Sex-stratified analyses were then performed using similar models to examine the presence of a potential sex difference. Results: The CONC group had an 82% (89% compatibility interval [CI] = 76 – 88%) likelihood of suffering a musculoskeletal injury within one year following return to sport compared to 73% (89% CI = 66 – 80%) for the MSI group and 56% (89% CI = 50 – 61%) for the CTL group. This translates to a 9 percentage point difference (89% CI = -0.01 – 0.18, 93.4% probability mass [PM] > 0) between the CONC and MSI groups, a 27 percentage point difference (89% CI = 0.18 – 0.35, 100% PM > 0) between the CONC and CTL groups, and a 18 percentage point difference (89% CI = 0.09 – 0.27, 100% PM > 0) between the MSI and CTL groups. Sex-stratified analyses demonstrated that males in the CONC group had an 86% (CI = 78 – 93%) probability of future injury compared to 75% (CI = 66 – 83%) among the female athletes in the CONC group. Conclusions: These results suggest that concussions pose the greatest risk of future injury, musculoskeletal injuries also pose a significant risk of injury following a return to sport. Furthermore, male athletes appear at greater risk for future injury following concussion than female athletes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".