Characterization of osteoarthritis in cats and meloxicam efficacy using objective chronic pain evaluation tools
Bibliographic record
Abstract
This study aimed to characterize osteoarthritis (OA)-related chronic pain and disability in experimental cats with naturally occurring OA. Peak vertical ground reaction force (PVF), accelerometer-based motor activity (MA) and the von Frey anesthesiometer-induced paw withdrawal threshold were used to define OA and to test the efficacy of meloxicam. A diagnosis of OA was based on radiographic and orthopedic examinations. Cats with OA (n=39) and classified as non-OA (n=6) were used to assess the reliability and sensitivity of the parameters to assess OA over 3weeks while being administered placebo medication. A randomised parallel design study was then used to investigate the effects on OA of daily oral meloxicam treatment for 4weeks at different dose rates (0.025mg/kg, n=10mg/kg; 0.04mg/kg, n=10; 0.05mg/kg, n=9), compared to cats administered a placebo (n=10). The test-retest repeatability for each tool was good (intra-class correlation coefficient ⩾0.6). The PVF and the von Frey anesthesiometer-induced paw withdrawal threshold discriminated OA (P<0.05). Meloxicam did not add to the PVF improvement observed in placebo-treated cats during the treatment period (adj-P⩽0.01). The 0.025 and the 0.05mg/kg meloxicam-treated cats experienced a higher night-time (17:00-06:58h) MA intensity during the treatment period compared to the placebo period (adj-P=0.04, and 0.02, respectively) and this effect was not observed in the placebo group. The high allodynia rate observed in the 0.04mg/kg meloxicam-treated group may explain the lower responsiveness to the drug. The von Frey anesthesiometer-induced paw withdrawal threshold demonstrated no responsiveness to meloxicam. The results from this study indicated that daily oral meloxicam administration for 4weeks provided pain relief according to night-time MA.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".