RETURN TO SPORT FOLLOWING CLOSED ACHILLES TENDON RUPTURE: A SYSTEMATIC REVIEW AND META-ANALYSIS OF ELIGIBLE STUDIES EVALUATING MANAGEMENT STRATEGIES, PATIENT FACTORS, AND LEVEL OF ATHLETIC ACTIVITY
Bibliographic record
Abstract
Introduction Achilles Tendon Rupture (ATR) is a prevalent injury in Western society. Much of the recent research has focused on measuring surgical methods and strength regained, rather than practical measures such as Return to Sport (RTS). A large systematic review was published in 2016 setting a benchmark RTS as 80%. The aim of this systematic review was to provide an up-to-date RTS following ATR. Methods PubMed and SPORTdiscuss databases were used to search for eligible studies published since 2017 that focused on closed Achilles tendon ruptures with clear definitions of return to sport and a minimum length of follow-up. The Newcastle-Ottawa grading tool was used to assess risk of bias in all included studies. Results Of 15 articles identified, 9 were ‘good’ and 6 were ‘fair’ after bias assessment, with none excluded for being poor. Return-to-sport (RTS) rate following Achilles tendon rupture was 76.76% (95% CI 74.19, 79.34 P= <0.001). Non-professional athletes had a higher RTS rate (78.29%; 95% CI 74.89, 81.68 P= <0.001) than professional athletes (74.91%; 95% CI 70.98, 78.85 P= <0.001). Surgical intervention resulted in a lower RTS rate (74.17%; 95% CI 70.74, 77.60 P= <0.001) than conservative management (70.00%; 95% CI 60.48, 79.52 P= <0.001). Conclusion These findings highlight the need to identify factors affecting RTS rates, including the type of management, level of sport, and patient-specific factors. Clinicians can use these findings to guide informed shared decision-making with patients regarding the long-term implications of ATR and to develop more targeted rehabilitation strategies for this injury.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".