Effect of Medial Meniscal Release on Tibial Translation After Tibial Plateau Leveling Osteotomy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the biomechanical effects of medial meniscal release (MMR) and medial, caudal pole hemimeniscectomy (MCH) on joint stability in the cranial cruciate ligament (CCL)-deficient canine stifle before and after tibial plateau leveling osteotomy (TPLO). STUDY DESIGN: Experimental study. ANIMALS: Thirty-one dogs. METHODS: In experiment 1, 16 pairs of normal hindlimbs randomly assigned to an intact or transected CCL group were studied to determine the magnitude of tibial translation after MMR and MCH under 20% body weight load using radiographic imaging of radio-opaque markers. In experiment 2, 15 pairs of CCL-deficient hindlimbs were randomly assigned to a TPLO or sham TPLO group. The remainder of the experiment was performed as described for experiment 1. The effect of CCL transection, MMR, MCH and TPLO were analyzed using 2-way repeated measures ANOVA; P<.05 was considered significant. RESULTS: We found a greater effect of MMR on tibial translation in transected CCL stifles than in intact stifles (P=.0016). We found no further effect of MCH after MMR (P>.05). We found a greater effect of MMR in sham TPLO than TPLO stifles (P=.0013) but no further effect of MCH after MMR (P>.05). CONCLUSIONS: By resisting tibial translation the medial meniscus might be at greater risk of tearing in CCL-deficient stifles. TPLO may spare the medial meniscus by neutralizing the tibial thrust and eliminating the wedge effect of the medial meniscus. CLINICAL RELEVANCE: MMR may not be indicated in the CCL-deficient stifle stabilized by TPLO.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".