Unrepaired ramp lesions are associated with a higher risk of secondary medial meniscus bucket handle tear compared to lateral meniscus bucket handle tear after anterior cruciate ligament reconstruction
Bibliographic record
Abstract
Abstract Purpose To compare the risk of a secondary bucket handle tear (BHT) of the medial and lateral menisci after an anterior cruciate ligament reconstruction (ACLR) with an unrecognized ramp lesion. The hypothesis was that an unrecognized ramp lesion would be associated with a secondary medial meniscus BHT more often than a lateral meniscus BHT. Methods A retrospective review of adults aged 18 or older who experienced a meniscal BHT after ACLR was conducted. An analysis of the clinical and radiological data from initial injury to revision surgery was completed. Two experts retrospectively documented the prevalence of ramp lesions present on preoperative magnetic resonance imaging (MRI) at the time of the index ACLR. The predictive value of a ramp lesion for BHT laterality was evaluated using logistic regression. Results Seventy‐six patients, 46 in the medial BHT group and 30 in the lateral BHT group, were included. A ramp lesion was present on the preoperative MRI in 33 patients in the medial BHT group compared to 13 in the lateral BHT group (p = 0.02, odds ratio: 3.2, 95% confidence interval: 1.2–8.0). In the logistic regression analysis, the only independent factor that predicted the occurrence of a medial BHT compared to a lateral BHT was the presence of a ramp tear on preoperative MRI before the index ACL surgery (logworth = 1.59; p = 0.03). Conclusion After a primary ACLR, an untreated ramp lesion was associated with a post‐operative medial BHT more often than a lateral BHT. Unrepaired ramp lesions may be a risk factor for subsequent medial meniscus BHT after primary ACLR. Level of Evidence Level IV.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".