Meniscal integrity predicts laxity of anterior cruciate ligament reconstruction
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the incidence of failure of anterior cruciate ligament (ACL) reconstruction and to assess the prognostic factors for such an outcome. METHODS: A prospective inception cohort of patients undergoing ACL reconstruction was analysed for failure (patient reported symptoms of rotational instability, a clinically positive pivot shift, MRI or arthroscopy showing ACL graft rupture). Risk factors evaluated included medial and lateral meniscal deficiency, medial and lateral meniscal repair, age, gender, BMI, graft size and time to surgery. Survival analysis was performed using the Kaplan-Meier method. Prognostic factors were assessed using the Cox proportional hazard model to investigate whether covariate risk factors influenced graft survival. RESULTS: One hundred and twenty-three patients were available for final analysis at a follow-up of 2 years. Eighteen patients satisfied the criteria of failure (15.4 %). Risk factors for failure were medial meniscal deficiency (hazard ratio 4.5; 95 % CI 1.8-11.5; p = 0.002), or lateral meniscal deficiency (hazard ratio 3.5; 95 % CI 1.3-9.3; p = 0.01). At 2-year follow-up, ACL survival was 94.5 % (95 % CI 89-100) for patients with intact menisci and 69 % (95 % CI 56-86) for those with deficiency of the medial or lateral meniscus (log-rank test p = 0.017). Patients were 4.9 times more likely to fail if they had a deficient medial or lateral meniscus. Those patients who underwent meniscal repair did not demonstrate any increased risk of failure. CONCLUSION: Medial and lateral meniscal tears are important prognostic factors that influence the survival of ACL reconstruction. Surgeons should endeavour to repair all meniscal tears associated with ACL reconstruction. LEVEL OF EVIDENCE: II.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".