The influence of age on the frequency of rhythmic masticatory muscle activity during sleep in general population differs from that in clinical research samples
Bibliographic record
Abstract
BACKGROUND: During sleep, limb and jaw muscle motor activity can be quantified by electromyography (EMG). The frequency of periodic limb activity during sleep increases with age in both the general and clinical research populations. The literature is controversial regarding stability, over age, of the frequency of rhythmic masticatory muscle activity (RMMA), which is one biomarker of sleep bruxism (SB). OBJECTIVES: The purpose of this retrospective sleep laboratory study was to assess if any change in RMMA frequency occurs over age in the general population (GP) and two clinical research (CR) samples. METHODS: RMMA signals from polysomnography (PSG) recordings of 465 individuals, irrespective of SB awareness, were analysed. The sample comprised 164 individuals from the GP of Sao Paulo, and 301 individuals from Montreal and Osaka CR samples. Data were divided into two subgroups, younger (15-39) and older (40-80) participants. RMMA was classified as low frequency (<2 events/h) or high (≥2 events/h). Pearson correlation (R) and B (slope) analyses were performed with power estimations. RESULTS: = .042; p < .001; 3.5 to 1.5 RMMA/h from 20 to 60 years old). CONCLUSIONS: In the GP, the RMMA index remained stable over age. In the CR samples, a significant, reduction was observed. Prospective studies with multiple home sleep recordings, in both general and clinical research populations, are needed before extrapolating from the present findings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".