Face and Predictive Validity of MI-RAT (Montreal Induction of Rat Arthritis Testing), a Surgical Model of Osteoarthritis Pain in Rodents Combined with Calibrated Exercise
Bibliographic record
Abstract
Validating animal pain models is crucial to enhancing translational research and response to pharmacological treatment. This study investigated the effects of a calibrated slight exercise protocol alone or combined with multimodal analgesia on sensory sensitivity, neuroproteomics, and joint structural components in the MI-RAT model. Joint instability was induced surgically on day (D) 0 in female rats (N = 48) distributed into sedentary–placebo, exercise–placebo, sedentary–positive analgesic (PA), and exercise–PA groups. Daily analgesic treatment (D3–D56) included pregabalin and carprofen. Quantitative sensory testing was achieved temporally (D–1, D7, D21, D56), while cartilage alteration (modified Mankin’s score (mMs)) and targeted spinal pain neuropeptide were quantified upon sacrifice. Compared with the sedentary–placebo (presenting allodynia from D7), the exercise–placebo group showed an increase in sensitivity threshold (p < 0.04 on D7, D21, and D56). PA treatment was efficient on D56 (p = 0.001) and presented a synergic anti-allodynic effect with exercise from D21 to D56 (p < 0.0001). Histological assessment demonstrated a detrimental influence of exercise (mMs = 33.3%) compared with sedentary counterparts (mMs = 12.0%; p < 0.001), with more mature transformations. Spinal neuropeptide concentration was correlated with sensory sensitization and modulation sites (inflammation and endogenous inhibitory control) of the forced mobility effect. The surgical MI-RAT OA model coupled with calibrated slight exercise demonstrated face and predictive validity, an assurance of higher clinical translatability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".