Multivariate Analysis of Morphometric Characteristics to Evaluate Risk Factors for Cranial Cruciate Ligament Deficiency in Labrador Retrievers
Bibliographic record
Abstract
OBJECTIVES: To determine the combination of conformation characteristics of the pelvic limbs of Labrador Retrievers that best discriminates between limb at risk to develop cranial cruciate ligament (CCL) disease and limbs at low risk using radiographs, computerized tomography (CT) images, and dual-energy X-ray absorptiometry (DEXA). STUDY DESIGN: Cross-sectional clinical study. ANIMALS: Twelve clinically normal and 9 unilaterally CCL-deficient Labrador Retrievers. METHODS: The pelvic limbs of normal dogs were considered as non-predisposed to CCL disease and the contralateral limbs of CCL-deficient dogs as predisposed. Conformation variables, obtained from femur and tibial radiographs, pelvic limb CT images and DEXA studies, of predisposed pelvic limbs were compared with the conformation variables from pelvic limbs of the low-risk group. An ROC curve analysis was used to assess the discriminating properties of conformation variables for several combinations. RESULTS: We determined that a combination of tibial plateau angle (TPA) and femoral anteversion angle (FAA) measured on radiographs was optimal for discriminating predisposed and non-predisposed limbs for CCL disease in Labrador Retrievers. CONCLUSIONS: Assessing predisposition to CCL disease with a combination of conformational measurements is better than using univariate parameters. In the future, TPA and FAA may be used to screen dogs suspected of being susceptible to CCL disease.
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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 | 0.003 |
| 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) | 0.002 | 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".