Effectiveness of Biofeedback in Individuals with Awake Bruxism Compared to Other Types of Treatment: A Systematic Review
Bibliographic record
Abstract
Excessive masticatory muscle activity is generally present in awake bruxism, which is related to increased anxiety and stress. It has been hypothesized that biofeedback could potentially manage awake bruxism, however, its effectiveness has not been empirically analyzed in a systematic manner. Therefore, this systematic review was designed to determine the effectiveness of biofeedback compared to other therapies in adults with awake bruxism. Extensive searches in five databases looking for randomized controlled trials (RCTs) that included biofeedback to manage awake bruxism were targeted. The risk of bias (RoB) assessment was conducted using the Cochrane RoB-2 tool. Overall, four studies were included in this systematic review, all of which used the electromyographic activity of the masticatory muscles during the day and night as the main endpoint. Auditory and visual biofeedback could reduce the excessive level of masticatory muscle activity in a few days of intervention. The majority of the included studies had a high RoB and only one study had a low RoB. The standardization of the biofeedback protocols was also inconsistent, which makes it difficult to establish the ideal protocol for the use of biofeedback in awake bruxism. Thus, it is proposed that future studies seek to reduce methodological risks and obtain more robust samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".