Clinicodemographic Risk Factors for Anterior Cruciate Ligament Injury: A Prospective 3-Cohort Study on Collegiate Varsity Athletes
Bibliographic record
Abstract
Background: Anterior cruciate ligament (ACL) injuries are among the most distressing injuries for collegiate varsity athletes. Identifying easily attainable clinicodemographic risk factors in this subgroup can help screen for high-risk athletes who may benefit from proven ACL injury risk reduction programs. Purpose: To identify clinicodemographic risk factors for noncontact ACL injury among female and male collegiate varsity athletes from 10 different sports. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 777 (276 female and 501 male athletes) collegiate varsity athletes from 3 consecutive seasons had an extended panel of clinicodemographic parameters recorded at their respective preseason physical sessions. The athletes were followed for 1 athletic season for noncontact ACL injuries. Results: Fifteen (6 female and 9 male athletes) athletes suffered a noncontact ACL injury during their season. Among all athletes, previous lower limb surgery and cutting sport participation were significantly associated with an increased risk of noncontact ACL injury. Among female athletes, previous ACL injury and previous lower limb surgery were significant risk factors. No significant clinicodemographic risk factors were identified in male athletes. Female sex was not a significant risk factor for noncontact ACL injury. Conclusion: The clinicodemographic risk factors for noncontact ACL injury identified in this study are easily attainable and may guide preseason screening for ACL injury risk in collegiate varsity athletes. The lack of association of these risk factors in male athletes may highlight the need to focus on other factors such as kinematics for these 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".