Internal rotational laxity of the stifle is increased in dogs predisposed to or affected by medial patellar luxation or cranial cruciate ligament disease
Bibliographic record
Abstract
Objective: To report and compare the range of stifle rotation in dogs predisposed to, or affected by, medial patellar luxation (MPL) or cranial cruciate ligament disease (CCLD) with that of Greyhounds. Methods: Greyhounds (negative controls), sound dogs of breeds predisposed to MPL or CCLD, and dogs with MPL or CCLD were enrolled in this clinical prospective study between December 5, 2022, and October 27, 2023. They underwent orthopedic examination and goniometric measurements of tibial torsion and stifle rotation, flexion, and extension. Limbs were classified as having low risk for, being predisposed to, or being affected by stifle disease. Body condition scores, tibial torsion, stifle rotation, flexion, and extension were compared between groups of limbs. Results: Data were collected on 208 limbs (104 dogs), classified as 48 controls, 37 limbs predisposed to MPL, 47 limbs predisposed to CCLD, 44 limbs with MPL, and 32 limbs with CCLD. Internal rotation was greater in stifles diagnosed with MPL (median, 38.83°; IQR, 35.67° to 46.83°) or CCLD (median, 32.17°; IQR, 25.67° to 35.67°) than in controls (median, 12.0°; IQR, 10.33° to 13.33°). Similar results were obtained when sound limbs predisposed to MPL (median, 32.22°; IQR, 27.67° to 35.67°) or CCLD (median, 32.17°; IQR, 25.67° to 35.67°) were compared to those of Greyhounds. Conclusions: Internal rotation exceeded that of control limbs by approximately 20° in stifles predisposed to or affected by MPL or CCLD. Clinical Relevance: These findings provide evidence of an association between rotational laxity and the presence of or predisposition to MPL and CCLD in dogs.
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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 | 0.001 | 0.001 |
| 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".