Return to Play After an Anterior Cruciate Ligament Reconstruction in the Collegiate Athlete: A Systematic Review Evaluating Return to Play Proportions and Associated Factors
Bibliographic record
Abstract
OBJECTIVE: To estimate anterior cruciate ligament reconstruction (ACLR) return-to-play (RTP) factors and proportions across all National Collegiate Athletics Association (NCAA) sports. DESIGN: Systematic review with prognosis and etiology components. LITERATURE SEARCH: Two independent reviewers searched PubMed, Cochrane Library, and Embase databases using terms related to RTP, ACLR, and NCAA for articles published up to June 30, 2023. STUDY SELECTION CRITERIA: Articles were included if RTP proportions or factors affecting RTP were reported and if the study population included NCAA collegiate athletes recovering from an ACLR. DATA SYNTHESIS: The proportion represents the total number of athletes who returned to play after ACLR over the total number of ACLR athletes from each cohort. The cumulative proportion represents the aggregated total from each included study. When eligibility information was available (ie, athletes in their final year of eligibility), RTP proportions were adjusted. The Newcastle-Ottawa Scale (NOS) was used to assess the study quality and scored by 2 raters. RESULTS: Nine studies were included. RTP criteria varied across the studies. Proportions of RTP ranged from 69% to 92%, with a cumulative RTP proportion after ACLR of 84% (628/745). The primary factors associated with the proportion of RTP were scholarship status, competitive eligibility remaining, depth chart position, and surgical graft type. CONCLUSIONS: The cumulative proportion of RTP was 84% and was associated with patient-specific and operative factors. Psychological and functional factors were not routinely reported, and rehabilitation protocols were unknown. Data were not explicitly available for any athletes outside of Division I. The criteria for RTP after ACLR varied. J Orthop Sports Phys Ther 2024;54(10):625-633. Epub 10 September 2024. https://doi.org/10.2519/jospt.2024.12483
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".