Mechanisms of craniofacial pain
Bibliographic record
Abstract
Aim To provide an overview of mechanisms underlying craniofacial pain; to highlight peripheral and central adaptations that may promote chronification of pain in craniofacial pain states such as migraine and temporomandibular disorders (TMD). Background Pain is a common symptom associated with disorders involving craniofacial tissues including the teeth and their supporting structures, the temporomandibular joint and the muscles of the head. Most acute painful craniofacial conditions are easily recognized and well managed, but others, especially those that are chronic (e.g., migraine, TMD and trigeminal neuropathies), present clinical challenges. Preclinical studies have provided substantial information about the anatomical and physiological mechanisms related to the initiation and modulation of nociceptive signals in the trigeminal system. While knowledge of the mechanisms underlying chronic craniofacial pain remains limited, both clinical and preclinical investigations suggest that changes in afferent inputs to the brain as well as in brain structure and modulatory pathways occur in chronic pain. Collectively, these changes result in amplification of nociception that promotes and sustains craniofacial chronic pain states. Conclusions The increased understanding gained of the physiological and pathological processing of nociception in the trigeminal system has provided new perspectives for the mechanistic understanding of acute craniofacial pain conditions and the peripheral and central adaptations that are related to pain chronification. Such knowledge may contribute to improvements in currently available treatments as well as to the development of novel analgesic therapies.
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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 | 0.002 | 0.001 |
| 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) | 0.010 | 0.003 |
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".