Snoring: the silent signal in sleep medicine
Bibliographic record
Abstract
ABSTRACT Snoring is highly prevalent, increases with age and is greater in men. The prevalence rates of snoring in the general population are variable, ranging from 20 to 60%. However, all population-based studies used the subjective analysis of snoring evaluated by questionnaires. Snoring is the major reported signal that may indicate obstructive sleep apnea (OSA). The polysomnography study, which is the golden standard test for the diagnosis of OSA, frequently uses the vibration recording by a sensor located on the patient’s neck. However, this signal is not calibrated and has no correlation with sound intensity. To date, there is no consensus on the methodology of the acquisition and analysis of the snore signal. This review article evaluates methodological studies that report on snoring recording equipment and correlate the snoring signal with OSA. Using the keywords “Snoring” and “Obstructive Sleep Apnea” in PubMed, we found 602 articles, however there were only 11 studies (1.8%) that reported on technical aspects regarding snoring recording and analysis. The selected articles showed a wide variability in the method of recording, as well as analysis of the snoring signal. The lack of technical studies on snoring reflects a major gap in Sleep Medicine. Recent data obtained by objective measurements showed that snoring intensity correlates with OSA severity. In addition, snoring recordings represent a promising new noninvasive tool for the diagnosis of OSA. We conclude that snoring is a poorly monitored signal and that more studies are warranted in this fundamental area of Sleep Medicine.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".