Morphometric characteristics of the pelvic limbs of Labrador Retrievers with and without cranial cruciate ligament deficiency
Bibliographic record
Abstract
OBJECTIVE-To evaluate skeletal characteristics of pelvic limbs with and without cranial cruciate ligament (CCL) deficiency in Labrador Retrievers. ANIMALS-30 adult purebred Labrador Retrievers. PROCEDURES-Pelvic limbs (n = 28) of 14 dogs without CCL deficiency were classified as control limbs, whereas the limbs of 16 dogs with CCL deficiency were considered affected by (18 limbs) or predisposed to (10 contralateral limbs of dogs with 1 affected limb) CCL deficiency. Skeletal characteristics were evaluated via physical examination, radiography, and computed tomography. Radiographic and computed tomographic variables were compared among limb groups by use of a mixed-model ANOVA. RESULTS-The tibial plateau slope was steeper in CCL-deficient limbs but not in predisposed limbs, compared with the slope in control limbs. The angle between diaphyseal and proximal tibial axes was increased in both CCL-deficient and predisposed limbs. The relative width of the proximal portion of the tibia and the inclination of the patellar ligament did not differ among limb groups. The overall and distal femoral anteversion angles were greater in CCL-deficient and predisposed limbs, whereas the femoral condyle trochanteric angle was decreased in those limb groups, compared with findings in control limbs. CONCLUSIONS AND CLINICAL RELEVANCE-Cranial angulation of the proximal portion of the tibia, excessive steepness of the tibial plateau, and distal femoral torsion appeared more likely to be associated with CCL deficiency than femoral angulation, tibial torsion, intercondylar notch stenosis, and increased inclination of the patellar ligament.
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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 | 0.000 |
| 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) | 0.002 | 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".