Research routes on awake bruxism metrics: Implications of the updated bruxism definition and evaluation strategies
Bibliographic record
Abstract
BACKGROUND: With time, due to the poor knowledge on it epidemiology, the need to focus on awake bruxism as a complement of sleep studies emerged. OBJECTIVE: In line with a similar recent proposal for sleep bruxism (SB), defining clinically oriented research routes to implement knowledge on awake bruxism (AB) metrics is important for an enhanced comprehension of the full bruxism spectrum, that is better assessment and more efficient management. METHODS: We summarised current strategies for AB assessment and proposed a research route for improving its metrics. RESULTS: Most of the literature focuses on bruxism in general or SB in particular, whilst knowledge on AB is generally fragmental. Assessment can be based on non-instrumental or instrumental approaches. The former include self-report (questionnaires, oral history) and clinical examination, whilst the latter include electromyography (EMG) of jaw muscles during wakefulness as well as the technology-enhanced ecological momentary assesment (EMA). Phenotyping of different AB activities should be the target of a research task force. In the absence of available data on the frequency and intensity of wake-time bruxism-type masticatory muscle activity, any speculation about the identification of thresholds and criteria to identify bruxers is premature. Research routes in the field must focus on the improvement of data reliability and validity. CONCLUSIONS: Probing deeper into the study of AB metrics is a fundamental step to assist clinicians in preventing and managing the putative consequences at the individual level. The present manuscript proposes some possible research routes to advance current knowledge. At different levels, instrumentally based and subject-based information must be gathered in a universally accepted standardised approach.
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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 | 0.528 | 0.656 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".