Chronic pain in cats: Recent advances in clinical assessment
Bibliographic record
Abstract
PRACTICAL RELEVANCE: Chronic pain is a feline health and welfare issue. It has a negative impact on quality of life and impairs the owner-cat bond. Chronic pain can exist by itself or may be associated with disease and/or injury, including osteoarthritis (OA), cancer, and oral and periodontal disease, among others. CLINICAL CHALLENGES: Chronic pain assessment is a fundamental part of feline practice, but can be challenging due to differences in pain mechanisms underlying different conditions, and the cat's natural behavior. It relies mostly on owner-assessed behavioral changes and time-consuming veterinary consultations. Beyond OA - for which disease-specific clinical signs have been described - little is known regarding other feline conditions that produce chronic pain. RECENT ADVANCES: Knowledge of the subject has, however, greatly improved in the past few years, informed by study of the mechanisms of pain in cats with OA and the development of pain scales that can be used by owners or veterinarians. Pain scales may facilitate the diagnosis and follow-up evaluation of chronic painful conditions, providing a basis for therapeutic decision-making. Assessment of quality of life is also recommended in cats with chronic pain, and its improvement can be used as a positive outcome in response to therapy. AIMS: This article reviews recent advances and presents the challenges and some future perspectives on clinical chronic pain assessment. The most common feline chronic conditions associated with pain are also described.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".