Relationships between craniofacial pain and bruxism*
Bibliographic record
Abstract
A still commonly held view in the literature and clinical practice is that bruxism causes pain because of overloading of the musculoskeletal tissue and craniofacial pain, on the other hand, triggers more bruxism. Furthermore, it is often believed that there is a dose-response gradient so that more bruxism (intensity, duration) leads to more overloading and pain. Provided the existence of efficient techniques to treat bruxism, it would be straightforward in such a simple system to target bruxism as the cause of pain and hence treat the pain. Of course, human biological systems are much more complex and therefore, it is no surprise that the relationship between bruxism and pain is far from being simple or even linear. Indeed, there are unexpected relationships, which complicate the establishment of adequate explanatory models. Part of the reason is the complexity of the bruxism in itself, which presents significant challenges related to operationalized criteria and diagnostic tools and underlying pathophysiology issues, which have been dealt with in other reviews in this issue. However, another important reason is the multifaceted nature of craniofacial pain. This review will address our current understanding of classification issues, epidemiology and neurobiological mechanisms of craniofacial pain. Experimental models of bruxism may help to further the understanding of the relationship between craniofacial pain and bruxism in addition to insights from intervention studies. The review will enable clinicians to understand the reasons why simple cause-effect relationships between bruxism and craniofacial pain are inadequate and the current implications for management of craniofacial pain.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".