Outcomes of Meniscal Repair in Patients Aged 40 and Above: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Meniscal injuries are increasingly common in older age groups. Age is often cited as a contraindication to undergoing meniscal repair due to concerns regarding failure rates. There has recently, however, been an increasing shift towards repair in older populations. The purpose of this study was to review outcomes of meniscal repair in patients over the age of 40. METHODS: A systematic search of the following databases was conducted of PubMed, SCOPUS, Web of Science, and Cochrane Library to identify studies reporting failure rates of patients over 40 with meniscal injuries undergoing repair. The definition of meniscus failure was noted for each study evaluated in this systematic review. Further data surrounding clinical and radiological outcomes were recorded and evaluated, when available. RESULTS: Thirteen studies were included in this review, encompassing a total of 316 meniscal repairs in patients over the age of 40 years. The overall failure rate was found to be 15.5% (49/316) (range 0-33.3%). There was no difference in the failure rate in those over 40 vs. under 40, and the two groups had equivalent functional outcomes. CONCLUSIONS: Age should not be considered a contra-indication for meniscal repair. Appropriately selected older patients can have acceptably low failure rates with meniscal repair and similar functional outcomes to those under the age of forty. Meniscal repairs in those over 40 achieved better functional outcomes than patients of the same age group who underwent meniscectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".