Meniscal Repair in the Setting of Revision Anterior Cruciate Ligament Reconstruction: Results From the MARS Cohort
Bibliographic record
Abstract
BACKGROUND: Meniscal preservation has been demonstrated to contribute to long-term knee health. This has been a successful intervention in patients with isolated tears and tears associated with anterior cruciate ligament (ACL) reconstruction. However, the results of meniscal repair in the setting of revision ACL reconstruction have not been documented. PURPOSE: To examine the prevalence and 2-year operative success rate of meniscal repairs in the revision ACL setting. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: All cases of revision ACL reconstruction with concomitant meniscal repair from a multicenter group between 2006 and 2011 were selected. Two-year follow-up was obtained by phone and email to determine whether any subsequent surgery had occurred to either knee since the initial revision ACL reconstruction. If so, operative reports were obtained, whenever possible, to verify the pathologic condition and subsequent treatment. RESULTS: In total, 218 patients (18%) from 1205 revision ACL reconstructions underwent concurrent meniscal repairs. There were 235 repairs performed: 153 medial, 48 lateral, and 17 medial and lateral. The majority of these repairs (n = 178; 76%) were performed with all-inside techniques. Two-year surgical follow-up was obtained on 90% (197/218) of the cohort. Overall, the meniscal repair failure rate was 8.6% (17/197) at 2 years. Of the 17 failures, 15 were medial (13 all-inside, 2 inside-out) and 2 were lateral (both all-inside). Four medial failures were treated in conjunction with a subsequent repeat revision ACL reconstruction. CONCLUSION: Meniscal repair in the revision ACL reconstruction setting does not have a high failure rate at 2-year follow-up. Failure rates for medial and lateral repairs were both <10% and consistent with success rates of primary ACL reconstruction meniscal repair. Medial tears underwent reoperation for failure at a significantly higher rate than lateral tears.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".