Preliminary Validation and Reliability Testing of the Montreal Instrument for Cat Arthritis Testing, for Use by Veterinarians, in a Colony of Laboratory Cats
Bibliographic record
Abstract
Subtle signs and conflicting physical and radiographic findings make feline osteoarthritis (OA) challenging to diagnose. A physical examination-based assessment was developed, consisting of eight items: Interaction, Exploration, Posture, Gait, Body Condition, Coat and Claws, (joint) Palpation-Findings, and Palpation-Cat Reaction. Content (experts) and face (veterinary students) validity were excellent. Construct validity, internal consistency, and intra- and inter-rater reliability were assessed via a pilot and main study, using laboratory-housed cats with and without OA. Gait distinguished OA status in the pilot ( p = 0.05) study. In the main study, no scale item achieved statistically significant OA detection. Forelimb peak vertical ground reaction force (PVF) correlated inversely with Gait (Rho s = -0.38 ( p = 0.03) to -0.41 ( p = 0.02)). Body Posture correlated with Gait, and inversely with forelimb PVF at two of three time points (Rho s = -0.38 ( p = 0.03) to -0.43 ( p = 0.01)). Palpation (Findings, Cat Reaction) did not distinguish OA from non-OA cats. Palpation-Cat Reaction (Forelimbs) correlated inversely with forelimb PVF at two time points (Rho s = -0.41 ( p = 0.02) to -0.41 ( p = 0.01)), but scores were highly variable, and poorly reliable. Gait and Posture require improved sensitivity, and Palpation should be interpreted cautiously, in diagnosing feline OA.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".