Evaluation of a scoring system based on conformation factors to predict cranial cruciate ligament disease in Labrador Retrievers
Bibliographic record
Abstract
OBJECTIVE: To determine the association of a radiographic score derived from tibial plateau angle (TPA) and femoral anteversion (FAA) with an outcome of cranial cruciate ligament deficiency (CCLD) in large dogs. STUDY DESIGN: Cross-sectional study. ANIMALS: 167 Labrador Retrievers. METHODS: Hind limbs of sound Labrador Retrievers over 6 years of age were considered at low risk for CCLD. Limbs were considered high risk for CCLD if they were affected or predisposed (sound contralateral limb in dogs with unilateral CCLD). The radiographic CCLD score was calculated for each limb. The TPA, FAA, and CCLD scores were compared between limbs of the same dog and between risk categories. A contingency table was used to evaluate the association of the CCLD score with the CCLD status of limbs. RESULTS: TPA, FAA, and CCLD scores were greater in limbs categorized as high risk for CCLD than in normal limbs. The sensitivity and specificity of the CCLD score was 87% and 79%, respectively. The positive predictive value was 69% and the negative predictive value was 92%. Scores were similar between paired right and left limbs, but did not agree for predicted status in 14/106 dogs. DISCUSSION: Our study supports an association between TPA, FAA, and CCLD in Labrador Retrievers. The negative predictive value of the CCLD score supports its application for screening dogs considered at low risk for CCLD. Positive CCLD scores should be interpreted with caution and the status of a dog may be undetermined if scores obtained on each limb disagree.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".