Meniscal root tears occur frequently in multi‐ligament knee injury and can be predicted by associated MRI injury patterns
Bibliographic record
Abstract
PURPOSE: The frequency of meniscal root tears in association with multi-ligament knee injury has not been established but adds to the complexity of surgical reconstruction and may have long-term consequences. Therefore, identifying root tears, on preoperative imaging, is important. The aim of this study was to identify the frequency of meniscal root tears, on preoperative magnetic resonance imaging, following multi-ligament injury and distinguish associated injury patterns that may aid detection. METHODS: Cases were identified from a prospectively collected institutional database. The magnetic resonance imaging of 188 multi-ligament injuries [median age 31 years (range 16-64)] was retrospectively reviewed by three musculoskeletal radiologists with the presence of meniscal injuries recorded alongside the ligament injury pattern and intra-articular fractures. Assessment of injury pattern was solely made on this imaging. RESULTS: 38 meniscal root injuries were identified in 37 knees (overall frequency = 20.2%; medial = 10.6%; lateral = 9.6%). The frequency of meniscal root tears was not increased in higher grade injuries (21.5% vs. 17.0%, n.s.). Valgus injury patterns were associated with lateral root tears (p < 0.05) and varus patterns were associated with medial root tears (p < 0.05). Further, fractures in the same compartment were associated with both medial and lateral root tears (p < 0.05). CONCLUSIONS: Meniscal root tears occur more frequently in multi-ligament knee injury than previously reported with isolated anterior cruciate rupture. Root tears can be predicted by ligament injury patterns and fractures sustained (suggestive of a compressive force). In multi-ligament cases, the preoperative magnetic resonance imaging can be used to detect these tears and associated patterns of injury. LEVEL OF EVIDENCE: 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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".