Do Meniscal Procedure Volumes Reflect Meniscal Tear Practice Guidelines? A Narrative Review Comparing Guidelines and Procedural Volume Data from the United States, Europe and Japan
Bibliographic record
Abstract
Purpose: This narrative review aimed to summarise international guidelines on the surgical management of meniscus tears and consider the extent to which they are reflected in clinical practice by reporting the respective trends in meniscus repair and resection procedure volumes.Methods: Two targeted reviews of the literature were conducted using a snowballing approach; the first sought to capture clinical guidelines covering meniscal repair, whilst the second aimed to identify data on meniscal repair procedure volumes.Results: Meniscal repair guidelines (n=7) and volume trends (n=5) were identified spanning Canada, France, Japan, the Netherlands, the United States and the United Kingdom, as well as Europe as a whole.Guidelines from different countries varied in the amount of detail included about different tear types, and the age ranges for which meniscal repairs-were considered most appropriate.Guidelines across different countries generally recommended the use of meniscal repairs where possible, on the basis that repairs are associated with better long-term outcomes compared with meniscectomies.Conclusion: Procedure volume trends showed that, in line with international guidelines, the rates of meniscal repair increased in the period considered.However, meniscectomy remained the most widely used approach to managing meniscal injuries over the period reviewed.This may have important consequences for patient outcomes and resource use in the long-term, as meniscectomy has been shown to be related to increased incidence of osteo-arthritis and total joint replacement.The inconsistency between guidelines and practice warrants further research into whether the guidelines reflect current medical opinion as well as how to improve compliance with best practice recommendations.
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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 | 0.015 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".