A century of bruxism research in top-ranking medical journals
Bibliographic record
Abstract
Background: Bruxism is a jaw-muscle activity characterized by teeth grinding and clenching. While many of its negative consequences (e.g., jaw-muscle pain, tooth fractures) are of particular interest to dentists, new insights underline the need for physicians to be knowledgeable about bruxism. In order to facilitate transfer of knowledge across disciplines, our objective was to assess what top-ranking medical journals have published on bruxism. Besides, we tested the insights described there against current science regarding the definition, assessment, epidemiology, etiology, consequences, comorbidities, and management of bruxism. Results: In the past century, the four top-ranking medical journals have provided their readership with various bits and pieces of information on bruxism. While some of these insights have withstood the test of time, others are somewhat outdated. Further, the identified publications provide an incomplete picture of what physicians should know. The present article helps reduce this knowledge gap. Conclusion: The role of the physician with regard to bruxism focuses mainly on its assessment and management, while insight into risk factors and comorbid conditions of bruxism is essential to high-level patient care. It is hoped that this article will contribute to improve the long-needed interdisciplinary collaboration between physicians and dentists regarding the assessment and management of bruxing patients.
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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 | 0.059 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.047 | 0.051 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".