Validity of self‐reported sleep bruxism among myofascial temporomandibular disorder patients and controls
Bibliographic record
Abstract
Sleep bruxism (SB), primarily involving rhythmic grinding of the teeth during sleep, has been advanced as a causal or maintenance factor for a variety of oro-facial problems, including temporomandibular disorders (TMD). As laboratory polysomnographic (PSG) assessment is extremely expensive and time-consuming, most research testing this belief has relied on patient self-report of SB. The current case-control study examined the accuracy of those self-reports relative to laboratory-based PSG assessment of SB in a large sample of women suffering from chronic myofascial TMD (n = 124) and a demographically matched control group without TMD (n = 46). A clinical research coordinator administered a structured questionnaire to assess self-reported SB. Participants then spent two consecutive nights in a sleep laboratory. Audiovisual and electromyographic data from the second night were scored to assess whether participants met criteria for the presence of 2 or more (2+) rhythmic masticatory muscle activity episodes accompanied by grinding sounds, moderate SB, or severe SB, using previously validated research scoring standards. Contingency tables were constructed to assess positive and negative predictive values, sensitivity and specificity, and 95% confidence intervals surrounding the point estimates. Results showed that self-report significantly predicted 2+ grinding sounds during sleep for TMD cases. However, self-reported SB failed to significantly predict the presence or absence of either moderate or severe SB as assessed by PSG, for both cases and controls. These data show that self-report of tooth grinding awareness is highly unlikely to be a valid indicator of true SB. Studies relying on self-report to assess SB must be viewed with extreme caution.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".