Evaluation of multiple radiographic predictors of cartilage lesions in the hip joints of eight-month-old dogs
Bibliographic record
Abstract
OBJECTIVE: To determine the radiographic methods that best predict the development of osteoarthritis in the hip joints of a cohort of dogs with hip dysplasia and unaffected dogs. ANIMALS: 205 Labrador Retrievers, Greyhounds, and Labrador Retriever-Greyhound crossbred dogs. PROCEDURE: Pelvic radiography was performed when the dogs were 8 months old. Ventrodorsal extended-hip, distraction, and dorsolateral subluxation (DLS) radiographs were obtained. An Orthopedic Foundation for Animals-like hip score, distraction index, dorsolateral subluxation score, and Norberg angle were derived from examination of radiographs. Osteoarthritis was diagnosed at the time of necropsy in dogs > or = 8 months of age on the basis of detection of articular cartilage lesions. Multiple logistic regression was used to determine the radiographic technique or techniques that best predicted development of osteoarthritis. RESULTS: A combination of 2 radiographic methods was better than any single method in predicting a cartilage lesion or a normal joint, but adding a third radiographic method did not improve that prediction. A combination of the DLS score and Norberg angle best predicted osteoarthritis of the hip joint or an unaffected hip joint. All models that excluded the DLS score were inferior to those that included it. CONCLUSIONS AND CLINICAL RELEVANCE: A combination of the DLS score and Norberg angle was the best predictor of radiographic measures in 8-month-old dogs to determine whether a dog would have normal or osteoarthritic hip joints.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".