Evaluation of accuracy of the Finnish elbow dysplasia screening protocol in Labrador retrievers
Bibliographic record
Abstract
OBJECTIVE: To determine whether the current Finnish screening method using a single flexed mediolateral view as scored by osteophyte is sufficient to diagnose mild elbow dysplasia in Labrador retrievers and to determine if an additional craniocaudal oblique projection would result in improvement in the screening protocol. MATERIALS AND METHODS: Thirteen dogs with one mildly affected elbow joint and one elbow joint without radiological evidence of osteophytes were studied. Radiographic and computed tomography studies were performed and the results compared with each other. RESULTS: Medial compartment disease was observed in 14 of 26 joints based on computed tomography. The sensitivity and specificity of the grading based mainly on osteoarthritis was 79 and 92%, respectively. A strong association existed between elbow dysplasia based on computed tomography and medial humeral epicondylar osteophytes on the craniocaudal projection. CLINICAL SIGNIFICANCE: A single mediolateral flexed radiograph is reliable in diagnosing mild elbow dysplasia in Labrador retrievers. However, the craniocaudal oblique projection increases the specificity of the diagnosis, and it is proposed that it be included in the radiographic protocol in this breed.
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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 | 0.011 | 0.022 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".