Digital analysis of ulnar trochlear notch sclerosis in Labrador retrievers
Bibliographic record
Abstract
OBJECTIVES: To compare ulnar trochlear notch bone radiopacity in Labrador retrievers with and without fragmented medial coronoid process using quantitative analysis of film density on digitised radiographs. METHODS: Mediolateral view elbow radiographs from Labrador retrievers (n=34) aged between six and 18 months were obtained and digitised. Images from dogs with an arthroscopic diagnosis of fragmentation of the medial coronoid process (n=17) were compared with that of a control population (n=17), and this data subject to statistical analysis. RESULTS: A statistically significant relationship between the presence of increased trochlear notch radiopacity and a fragmented medial coronoid process was identified. Fractional analysis of this area shows the region of greatest difference in radiopacity between normal and fragmented medial coronoid process cohorts to be in the trochlear region of the medial coronoid process of the ulna. A decrease in radiopacity values in the dysplastic group versus the normal cohort was observed for the region of the proximo-caudal ulnar trochlear notch. CLINICAL SIGNIFICANCE: An increase in ulnar trochlear notch radiopacity is a finding associated with fragmentation of the medial coronoid process in Labrador retrievers.
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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 | 0.002 | 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".