Pathophysiology of TMD pain - basic mechanisms and their implications for pharmacotherapy
Bibliographic record
Abstract
This article discusses the pathophysiology of temporomandibular disorders (TMD)-related pain and its treatment with analgesic drugs. Temporomandibular disorders are comprised of a group of conditions that result in temporomandibular joint pain (arthralgia, arthritis) and/or masticatory muscle pain (myofascial TMD). In at least some patients with TMD, a peripheral mechanism contributes to this pain. However, there is often a poor correlation between the severity of TMD-related pain complaints and evidence of definitive tissue pathology. This has led to the concept that pain in some patients with TMD may result from altered central nervous system pain processing and further that this altered pain processing may be attributable to specific genes that are heritable. Psychosocial stressors are also thought to contribute to the development of TMD-related pain, particularly masticatory muscle pain. Finally, substantially more women suffer from TMD than men. Although there are arguably multiple reasons for sex-related differences in the prevalence of TMD, one candidate for the increased occurrence of this disorder in women has been suggested to be the female sex hormone oestrogen. Analgesic drugs are an integral part of the primary treatment for TMD-related pain and dysfunction with more that 90% of treatment recommendations involving use of medications. The most commonly used agents include non-steroidal anti-inflammatory drugs, corticosteroids, muscle relaxants, anxiolytics, opiates and tricyclic antidepressants, however, evidence in support of the effectiveness of these drugs is lacking. Continued research into the pathophysiology of TMD-related pain and the effectiveness of analgesic treatments for this pain is required.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".