Meniscal Repair in the Setting of Revision Anterior Cruciate Ligament Reconstruction: 6-Year Follow-up Results From the MARS Cohort
Bibliographic record
Abstract
Background: Meniscal preservation has been demonstrated to contribute to long-term knee health and has been a successful intervention in isolation and in patients with anterior cruciate ligament reconstruction (ACLR). The long-term results of meniscal repair in the setting of revision ACLR have yet to be documented. Purpose: To report the incidence of meniscal repair failures at the 6-year follow-up in a cohort of patients who underwent concurrent revision ACLR and primary meniscal repair. Study Design: Prospective cohort study; Level of evidence, 2. Methods: All revision ACLRs with concomitant primary meniscal repair cases from a multicenter group between 2006 and 2011 were selected. Six-year follow-up was obtained to determine whether any subsequent surgery had occurred since their initial revision ACLR. If so, operative reports were obtained, whenever possible, to verify pathological condition and treatment. Results: In total, 221 patients from 1234 revision ACLRs underwent concurrent primary meniscal repairs (18% of the cohort). There were 238 repairs performed: 173 medial and 65 lateral. The majority of these repairs (n = 181; 76%) were performed with an all-inside technique. Six-year surgical follow-up was obtained in 77% (171/221) of the cohort, or 189 of 238 (79%) of the repairs (136 medial, 53 lateral). The meniscal repair failure rate, defined as reoperation, was 16% (31/189) at 6 years. Of the 31 failures, 28 were medial (24 all-inside, 4 inside-out; 28/136 = 20.6% failure rate) and 3 were lateral (2 all-inside, 1 inside-out; 3/53 = 5.7% failure rate). Three medial failures were treated in conjunction with a subsequent repeat revision ACLR. Medial tears underwent reoperation for failure at a significantly higher rate than lateral tears (20.6% vs 5.7%; P = .01) and had a significantly shorter survival time compared with lateral tears ( P = .02). No difference was found between the failure and nonfailure groups when it came to tear type, tear length, repair technique utilized, suture/implant type, or number of sutures used between the 2 groups. Conclusion: Meniscal repair in the revision ACLR setting has a 16% failure rate at 6 years. Failure rates for medial tears (20.6%) were found to be higher than that for lateral tears (5.7%), which aligns with previous studies in both the revision and primary ACLR setting.
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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 | 0.001 | 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".