Effects of risk factors on evoked pain patterns in rat models of experimental knee osteoarthritis
Bibliographic record
Abstract
Pain experiences in patients with knee osteoarthritis (OA) may be influenced differently by OA risk factors, reducing the translatability of preclinical research into the clinic. Our objective was to contrast evoked pain patterns after exposure to different OA risk factors including acute joint trauma, chronic instability, or obesity/metabolic syndrome using rat models of experimental knee OA. We tested longitudinal patterns of evoked pain behaviors (knee pressure pain threshold and hindpaw withdrawal threshold) in young male rats exposed to different OA-inducing risk factors including (1) nonsurgical joint trauma (impact-induced anterior cruciate ligament (ACL) rupture); (2) surgical joint destabilization (ACL + medial meniscotibial ligament transection); and (3) high fat/sucrose (HFS) diet-induced obesity. Histopathology for synovitis, cartilage damage, and subchondral bone morphology was performed. Pressure pain threshold was reduced (more pain) most, and earlier by joint trauma (Week 4-12) and HFS (Week 8-28) than by joint destabilization (Week 12). Hindpaw withdrawal threshold was reduced transiently after joint trauma (Week 4), with smaller and later reductions after joint destabilization (Week 12), but not with HFS. Synovial inflammation occurred at Week 4 after joint trauma and instability but only coincided with pain behaviors after joint trauma. Cartilage and bone histopathology were most severe after joint destabilization and least severe with HFS. The pattern, intensity, and timing of evoked pain behaviors varied due to OA risk factor exposure and were inconsistently associated with histopathological OA features. These findings may help to explain the challenges with translating preclinical OA pain research to multimorbid clinical OA contexts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".