Updating the Bruxism Definitions: Report of an International Consensus Meeting
Bibliographic record
Abstract
BACKGROUND: Bruxism is receiving increasing attention from both clinicians and researchers over the past decades. Recently, it has become clear that some aspects of the currently proposed, expert-driven bruxism definitions raise questions and cause confusion among clinicians, researchers, educators and patients. OBJECTIVES: The aim of this report is threefold: (1) to provide the reader with a glossary of the existing definitions, (2) to discuss frequently asked questions regarding these definitions and (3) to suggest a road map for the next steps to be taken towards a better understanding of bruxism. MATERIAL AND METHODS: A closed (invitation-only) full-day workshop at the 2024 General Session & Exhibition of the International Association for Dental, Oral and Craniofacial Research (IADR) convened international bruxism experts to discuss the current definitions. Insights from these discussions were compiled, analysed and summarised. RESULT: The present report provides a glossary of the constituent terms of the currently proposed definitions, an overview of the frequently asked questions and insights into the next steps to be taken. By current consensus and to avoid any further confusion, the addendum 'in otherwise healthy individuals' has been removed from the specific definitions of sleep and awake bruxism. In addition, the grading system's hierarchical organisation, as proposed previously, was revised and clarified, proposing the inclusion of terms based on self-reporting, clinical examination and device-based assessment tools. CONCLUSION: To ascertain that we all use the same terminology, we recommend using the current publication when referring to the definitions of bruxism and its constituent terms.
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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 | 0.367 | 0.311 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.021 | 0.023 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".