Bibliographic record
Abstract
Chronic osteoarthritis (OA) pain is a complex nociplastic condition that affects humans, as well as cats and dogs. This review summarizes the physiology of pain in healthy individuals, the physiopathology of OA pain, and the use of quantitative sensory testing (QST) to objectively assess somatosensory sensitization associated with chronic OA pain. It discusses the translation of human OA pain phenotype profiles to animals, the management of neuro-sensitization with currently prescribed treatments, and complementary methods for evaluating neuro-sensitization, such as electrodiagnostic testing. Additionally, this review serves as a practical guide for standardizing QST in rats, cats, and dogs, with explanatory appendices. It was hypothesised that in translation with the human condition, OA-induced rat models and naturally occurring OA in cats and dogs would exhibit similar somatosensory sensitization profiles. As observed in human OA, imbalance between facilitatory and inhibitory endogenous controls is present in animal OA, is traced with QST and governs different nociceptive phenotypes. Confirming and validating OA pain profiles will promote a patient-tailored approach to effectively alleviate neuro-sensitization in humans and animals.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.107 | 0.128 |
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".