Does Radiographic Arthrosis Correlate With Cartilage Pathology in Labrador Retrievers Affected by Medial Coronoid Process Disease?
Bibliographic record
Abstract
OBJECTIVE: To compare radiographic elbow arthrosis with arthroscopic cartilage pathology in Labrador retrievers with elbow osteoarthritis secondary to medial coronoid process (MCP) disease. STUDY DESIGN: Retrospective epidemiological study. ANIMALS: Labrador retrievers (n = 317; 592 elbow joints). METHODS: Data were collected retrospectively (June 2007-June 2011) to identify Labrador retrievers with thoracic limb lameness and elbow pain, a complete set of elbow radiographs, and a comprehensive arthroscopic surgery report. Each radiograph was scored for osteophytosis on the anconeal process and ulnar subtrochlear sclerosis using a modification of the International Elbow Working Group (IEWG) scoring system. Elbows affected by traumatic MCP fracture, humeral condylar osteochondrosis, or ununited anconeal process were excluded. The arthroscopic report was used to generate a composite cartilage score (CCS; 0 = normal, 1 = mild, 2 = moderate, 3 = severe) for each elbow joint. Ordinal regression analysis was performed to test the relationship between radiographic arthrosis score and CCS. RESULTS: There was a significant relationship between radiographic elbow arthrosis and CCS (P < .001). Elbows with a higher radiographic score were significantly more likely to have a higher CCS than elbows with a lower radiographic score. For every month increase in age, the odds of having a higher CCS increased by 0.016 (1.6%). CONCLUSIONS: Radiographic arthrosis can be used to predict the severity of arthroscopic cartilage pathology in Labrador retrievers affected by MCP 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".