Examining Return to Play Protocols for ACL Injuries using the International Classification of Functioning, Disability, and Health (ICF): A Rapid Review
Bibliographic record
Abstract
Abstract Purpose Anterior cruciate ligament (ACL) injuries are a common occurrence, especially in sports. These injuries require a comprehensive return-to-play (RTP) protocol that is suited for the individual. This review aims to assess existing RTP protocols for ACL injuries, using the WHO’s International Classification of Functioning, Disability, and Health (ICF) framework. The objective is to identify trends and gaps in RTP protocols based on the domains of the ICF framework. Methods A rapid review was conducted from the following databases: Embase, MEDLINE, and CENTRAL. Studies were screened using Covidence and reviewed using National Collaborating Centre for Methods and Tools (NCCMT) guidelines. The analysis examined the included return-to-play protocols and assessed them through the lens of the ICF framework. Results Fifteen studies were included in the review. Based on the protocols of the included studies, three key trends were observed: 1) Focus on functioning and disability rather than contextual factors, 2) Player’s psychological needs are considered in only a few studies (n = 4), and 3) Most return-to-play protocols were fairly rigid. Conclusion This review highlights key trends and gaps in existing RTP protocols for ACL injuries. The protocols can be improved by aligning themselves with the ICF, specifically through the inclusion of environmental and personal factors. This could potentially pave the way for a standardized ACL RTP protocol. Level of Evidence: II
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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 | 0.027 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".