Unravelling the relationship between age, nociception and joint destruction in naturally occurring osteoarthritis of Dunkin Hartley guinea pigs
Bibliographic record
Abstract
Osteoarthritis (OA) is a debilitating and painful disease, the incidence of which increases with advancing age. One of the confounding aspects of OA is that there is a disconnect between the severity of joint degeneration and the intensity of pain reported. This study examined the relationship between age, joint nociception, and joint pathology in an animal model of naturally occurring OA. Dunkin Hartley guinea pigs were grouped according to age: young (2-5 months) and senescent (17-37 months). Joint nociception was objectively measured in these animals by recording electrophysiologically from knee joint primary afferents in response to non-noxious and noxious movements of the knee. Joint pathology in the same knees was then determined by histomorphology and micro-computerized tomography (micro-CT). A principal components analysis was carried out on the data to determine if any correlation exists between each of the measured variables. In aged guinea pigs, 33% of joint mechanosensory nerves were spontaneously active, whereas young animals showed no such neural activity at rest. The frequency of afferent firing evoked by noxious movements was greater in old guinea pigs. Micro-CT and histopathological determination of OA positively correlated with age; however, there was no significant correlation between the severity of joint degeneration and nociception. In the Dunkin Hartley model of inveterate OA, the level of joint pathology correlates well with increasing age. This study also provides the first objective evidence that there is no correlation between joint nociception and articular damage, thereby corroborating the clinical observation that pain is a poor predictor of OA severity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".