Manuela Lefort-Holguin_2025_Neuro-sensitization
Bibliographic record
Abstract
Feline osteoarthritis (OA) is characterized by somatosensory neuro-sensitization, which can be assessed through quantitative sensory testing. It was hypothesized that somatosensory neuro-sensitization would increase with OA severity, as categorized through the validated Montreal Instrument for Cat Arthritis Testing, for Veterinarians (MI-CAT(V)). Healthy (n=10) and cats with naturally occurring OA (n=121) were enrolled in this prospective, negatively controlled study. Peripheral and spinal sensitization were respectively assessed by paw withdrawal threshold (PWT) and response to mechanical temporal summation (RMTS). PWT determined allodynia threshold. Derived from MI-CAT(V), cats were sorted into four validated OA severity clusters, from absent to severe OA. Outcomes were compared across allodynia status (healthy, non-allodynic and allodynic) and OA clusters, while testing for the influence of demographic data, with alpha set at 5%. The PWT, RMTS, MI-CAT(V) outcomes and age accurately discerned between healthy and OA animals (P<0.002), but not body weight. Non-allodynic cats had similarly altered MI-CAT(V) and RMTS to allodynic cats, but they were younger (P=0.010) and had a higher PWT than allodynic (P<0.001), and similar PWT (P=0.925) but older (P<0.001) than healthy cats. Spinal sensitization was similar in the three OA-affected clusters (mild-moderate-severe; P=1.000), but MI-CAT(V) categorized them sensitively (P<0.001). The mild cluster included more non-allodynic cats than the moderate (P=0.021) and severe (P=0.026) clusters. Interestingly, 32% of mild OA cats were allodynic, when the proportion increased to 61% in pooled moderate/severe OA cats (P=0.013). OA cats are sensitized compared to healthy cats, and peripheral sensitization seems to increase with OA severity, which influences pain phenotype.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.110 | 0.026 |
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".