Chronic Orofacial Pain: Models, Mechanisms, and Genetic and Related Environmental Influences
Bibliographic record
Abstract
Chronic orofacial pain conditions can be particularly difficult to diagnose and treat because of their complexity and limited understanding of the mechanisms underlying their aetiology and pathogenesis. Furthermore, there is considerable variability between individuals in their susceptibility to risk factors predisposing them to the development and maintenance of chronic pain as well as in their expression of chronic pain features such as allodynia, hyperalgesia and extraterritorial sensory spread. The variability suggests that genetic as well as environmental factors may contribute to the development and maintenance of chronic orofacial pain. This article reviews these features of chronic orofacial pain, and outlines findings from studies in animal models of the behavioural characteristics and underlying mechanisms related to the development and maintenance of chronic orofacial pain and trigeminal neuropathic pain in particular. The review also considers the role of environmental and especially genetic factors in these models, focussing on findings of differences between animal strains in the features and underlying mechanisms of chronic pain. These findings are not only relevant to understanding underlying mechanisms and the variability between patients in the development, expression and maintenance of chronic orofacial pain, but also underscore the importance for considering the strain of the animal to model and explore chronic orofacial pain processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".