Face validity of a proposed tool for staging canine osteoarthritis: Canine OsteoArthritis Staging Tool (COAST)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Osteoarthritis (OA) is a common, progressive degenerative disease of synovial joints. It can develop subsequent to an acquired disorder such as joint trauma but is primarily driven by developmental orthopedic disease in young dogs. Therefore, it is essentially characterised as an early onset but lifelong disease that worsens with age. Early intervention using a multi-modal drug and non-drug approach, with or without surgery as required, has the greatest potential for the most effective management of the disease. Timely implementation of a continuing care plan provides an opportunity to slow the rate of deterioration by reducing the negative impacts of OA-associated pain, encouraging appropriate levels of activity and improving strength and posture. Unfortunately, many dogs are presented to veterinary clinics only when marked behavioural changes are observed and substantial deterioration of the musculoskeletal and somatosensory systems has already occurred. To assist veterinarians with early and stage-specific diagnosis of OA in dogs, the authors present a proposed, practical diagnostic aid called 'COAST' (Canine OsteoArthritis Staging Tool) with face validity. As indicated by the successful implementation of staging systems for other companion animal diseases, it is expected that standardized staging of OA in dogs will help guide disease management plans and improve monitoring. The items used to construct COAST have been developed using consensus opinion of international experts from nine countries, who are actively working in the fields of small animal orthopaedics, anaesthesia and pain management. Further validation (test-retest, discriminatory ability, responsiveness, criterion validation) of the tool under field conditions is now required and the authors invite input.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it