Anterior Cruciate Ligament Reconstruction Return-to-Sport Decision-Making: A Scoping Review
Bibliographic record
Abstract
CONTEXT: Clinical guidelines support the use of testing batteries to assess athlete readiness for return to sport (RTS) and risk of reinjury after anterior cruciate ligament (ACL) reconstruction (ACL-R). There is no consensus on the composition of the testing batteries. Test selection is based mainly on commonality in research, personal preference, and equipment availability. Including athletic performance assessments (APA) used in the athlete's sport may assist RTS decision-making for stakeholders. OBJECTIVE: To determine whether APA for speed, agility, strength, or cardiovascular endurance are (1) used in ACL-R RTS literature and (2) indicative of RTS or reinjury rates. DATA SOURCES: A systematic search was performed in MEDLINE, EMBASE, CINAHL, SPORTDiscus, Scopus, Web of Science, and ProQuest Dissertations and Theses Global. STUDY SELECTION: Eligibility criteria were as follows: (1) athletes between 6 months and 2 years post-ACL-R, (2) commonly used APA, (3) peer-reviewed primary study with original published data. STUDY DESIGN: Scoping Review. LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: A total of 17 studies included 24 instances of APA with a high degree of heterogeneity for both tests and protocols. RESULTS: Agility makes up 75% of the APA. Only 17.6% of studies reported RTS or reinjury rates, none of which reported a significant relationship between these rates and APA outcomes. CONCLUSION: Speed, strength, and cardiovascular endurance tests are underrepresented in ACL-R RTS literature. Compared with healthy controls, deficits in APA results for ACL-R athletes were common; however, many studies reported significant improvements in results for ACL-R athletes over time. There is some evidence that well-trained ACL-R athletes can match the performance of uninjured athletes in high-level sports.
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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 | 0.033 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".