Temporomandibular Joint Cytokine Profiles in the Horse
Bibliographic record
Abstract
It has been suggested that dental abnormalities lead to temporomandibular joint inflammation and pain that may be mitigated by regular dental care. There is considerable literature on the pathophysiology of equine joint disease including studies on cytokine profiles in diseased appendicular joints. This study examined the effects of age and dental malocclusions summarized as a dental pathology score on equine temporomandibular joint cytokine (IL-1, IL-6, IL-8, TNF alpha and TGF-beta1, -beta2, -beta3) concentrations. TGF-beta3 was not detected in any joint sample. IL-1, IL-6 and TNF alpha were not influenced by age. Foals had significantly lower concentrations of lL-8 and TGF-beta1, and higher levels of TGF-beta2 compared with older horses. Age did not effect cytokine concentration in older horses although there was a trend towards increasing 1L-8 with age. The dental pathology score increased with age in mature horses, however there was no effect of dental pathology score on cytokine concentration. There was no effect of incisor eruption, and presence or number of periodontal lesions on temporomandibular joint cytokine concentration. Our findings indicate that age but not dental pathology affected temporomandibular joint proinflammatory cytokine concentration in this population of horses.
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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 | 0.000 | 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".