Does sex affect second ACL injury risk? A systematic review with meta-analysis
Bibliographic record
Abstract
Objective To determine sex-based differences in risk of a second ACL injury (overall and by laterality) following primary ACL reconstruction in athletes who are attempting to return to sport. Design Systematic review with meta-analysis. Data sources Systematic search of five databases conducted in August 2019. Eligibility criteria for selecting studies Studies reporting sex-based differences in the incidence of second ACL injury in athletes attempting to return-to-sports and who were followed for at least 1 year following primary ACL reconstruction. Results Nineteen studies were included in this review, with seven studies excluded from the primary meta-analysis due to high risk of bias. The remaining 12 studies (n=1431 females, n=1513 males) underwent meta-analysis, with all 19 studies included in a sensitivity analysis. Total second ACL injury risk was 21.9% (females: 22.8%, males: 20.3%). Females were found to have 10.7% risk of an ipsilateral ACL injury and 11.8% risk of a contralateral ACL injury. Males were found to have 12.0% risk of an ipsilateral ACL injury and 8.7% risk of a contralateral ACL injury. No statistically significant differences were observed for total second ACL injury risk (risk difference=−0.6%, 95% CI −4.9 to 3.7, p=0.783, I 2 =41%) or contralateral ACL injury risk (risk difference=1.9%, 95% CI −0.5% to 4.4%, p=0.113, I 2 =15%) between sexes. Females were found to have a 3.4% absolute risk reduction in subsequent ipsilateral ACL injury risk compared with males (risk difference=−3.4%, 95% CI −6.7% to −0.02%, p=0.037, I 2 =35%). Conclusion Both sexes have >20% increased risk of experiencing a second ACL injury. Any difference in the absolute risk of either a subsequent ipsilateral or contralateral ACL injury between sexes appears to be small. Registration PROSPERO (CRD42020148369)
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.028 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".