Comparison of three radiographic methods for diagnosis of hip dysplasia in eight-month-old dogs
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of the extended-hip radiographic (EHR) score, the distraction index (DI), and the dorsolateral subluxation (DLS) score for identifying hip dysplasia in dogs at 8 months of age. DESIGN: Cohort study ANIMALS: 129 Labrador Retrievers, Greyhounds, and Labrador Retriever-Greyhound crossbreds. PROCEDURE: Radiography was performed when dogs were 8 months of age. Dogs were euthanatized at 8 to 36 months of age; hip dysplasia was diagnosed at the time of necropsy on the basis of results of a gross examination of the articular cartilage of the hip joints for signs of osteoarthritis. RESULTS: The EHR score, DI, and DLS score at 8 months of age were all significantly correlated with degree of cartilage degeneration at necropsy. Sensitivity and specificity of using EHR score at 8 months of age to diagnose hip dysplasia (scores > 3 were considered abnormal) were 38 and 96%, respectively; sensitivity and specificity of using DI (values > 0.7 were considered abnormal) were 50 and 89%; and sensitivity and specificity of using DLS score (scores < 55% were considered abnormal) were 83 and 84%. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that specificities of the 3 methods for diagnosing hip dysplasia in dogs at 8 months of age were similar. However, the DLS score had higher sensitivity, indicating that there were fewer false-negative results.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".