Comparing Severe Injuries by Sex and Sport in Collegiate-Level Athletes: A Descriptive Epidemiologic Study
Bibliographic record
Abstract
Context: There is a lack of research on sex differences for severe injuries across a variety of sports at the collegiate level. Objective: To compare differences in injury severity and concussion between sexes and collegiate sports. Design: Descriptive epidemiological study. Participants: 1,657 injuries were analyzed from collegiate teams at York University. Data Collection and Analysis: Injuries were assessed by a certified or student athletic therapist and were categorized based on degree of tissue and/or joint damage as either severe or nonsevere. Severe injuries included those with third degree damage, while all others were classified as nonsevere. Injury severity was compared between the sexes and across different sports using Pearson chisquare analysis. Logistic regression was used to assess the relative contribution of each covariate. Results: Males sustained 1,155 injuries, with 13.3% of them being severe, while females sustained only 502 injuries, 17.7% of which were severe. The odds of sustaining severe injuries among female athletes are 1.4 times the odds of male athletes (OR: 1.40, CI 1.05−1.86). Eleven percent of all female injuries were concussions—significantly more than males (χ 2 = 11.03, p = .001). The odds of female athletes having a concussion are 1.9 times the odds of a male athlete (OR: 1.85, CI 1.28−2.67). Conclusion: Based on our analysis, females are at an increased risk of sustaining a severe injury, particularly concussions. These findings highlight the need for future research into sex and sport-specific risk factors. This may provide information for health care professionals, coaches, and athletes for the proper prevention, on-field care, and treatment of sport injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".