Prehabilitation prior to anterior cruciate ligament reconstruction is a safe and effective intervention for short‐ to long‐term benefits: A systematic review
Bibliographic record
Abstract
PURPOSE: Comprehensively explore current practices in preoperative rehabilitation (prehabilitation) for anterior cruciate ligament reconstruction (ACLR) and assess corresponding clinical outcomes and complication rates. METHODS: A systematic search of EMBASE, MEDLINE, Cochrane and PubMed was conducted from inception to 1 November 2024. All studies reporting outcomes and/or complications following prehabilitation and ACLR were included. Screening and data abstraction were designed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Revised Assessment of Multiple Systematic Reviews guidelines. RESULTS: Thirty-six studies with 2326 patients undergoing prehabilitation and ACLR were included. Weighted averages of all clinical outcomes met or surpassed patient acceptable symptom state (PASS) thresholds and return to sports (RTS) criteria. There were no preoperative complications following prehabilitation. Major post-operative complications included graft failure (4.6%), contralateral ACL rupture (1.0%), surgical site infection (0.6%), deep infection (0.4%), non-ACL ligament injury (0.5%), reoperation for hardware removal (0.3%), muscle rupture (0.1%), patellar subluxation (0.1%) and patellar rupture (0.1%). CONCLUSION: Current prehabilitation practices for ACLR emphasize impairment resolution, ROM restoration, and neuromuscular exercises. Safety of current practices is supported by the absence of preoperative complications and similar post-operative complication rates compared to patients undergoing standard care. Clinical outcomes of patients undergoing prehabilitation were shown to meet and surpass PASS thresholds and RTS criteria, expedite post-operative recovery, and maintain functional improvements up to 10 years post-operation, suggesting that prehabilitation is a safe and effective intervention yielding short- to long-term benefits. There is a need for further high-quality randomized controlled trials and large prospective cohort studies comparing the effect of prehabilitation on post-operative outcomes, reporting specific exercise details and protocol progression. LEVEL OF EVIDENCE: Level 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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".