Efficacy and Long-Term Outcomes of Arthroscopic Meniscus Repair: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The objective of this systematic review and meta-analysis was to evaluate the efficacy and long-term outcomes of arthroscopic meniscus repair, focusing on success, failure, and reoperation rates. A comprehensive literature search was conducted across PubMed, EMBASE, Cochrane Library, and Scopus, including studies that involved patients undergoing arthroscopic meniscus repair with a minimum follow-up of two years. The quality of the included studies was assessed using the Cochrane Risk of Bias Tool for randomized controlled trials and the Newcastle-Ottawa Scale for observational studies. Meta-analyses were conducted using RStudio 4.3.1 software (RStudio Inc., Boston, MA), with pooled risk ratios (RR) and 95% confidence intervals (CIs) calculated for dichotomous outcomes using a random effects model. The meta-analysis included 10 studies totaling 1,004 patients. The pooled success rate for arthroscopic meniscus repair was 83% (95% CI: 77%-89%), while the pooled failure rate was 20% (95% CI: 15%-25%), and the pooled reoperation rate was 21% (95% CI: 17%-25%). Significant heterogeneity was observed across studies (I² > 50%). Subgroup analyses based on suture techniques and concurrent anterior cruciate ligament (ACL) reconstruction did not reveal significant outcome differences. Arthroscopic meniscus repair demonstrates high success rates and acceptable failure and reoperation rates, supporting its continued use in clinical practice. However, the variability in study quality and significant heterogeneity highlight the need for more rigorous, high-quality studies to refine techniques and better explore long-term outcomes.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.046 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".