Detailed analysis of the relationship between tracheal breath sounds and airflow in relation to OSA during wake and sleep
Bibliographic record
Abstract
Tracheal respiratory sound analysis is a simple, inexpensive and non-invasive way to study the pathology of the upper airways. Recently, it has attracted considerable attention for acoustical flow estimation and investigation of obstruction in the upper airways. Obstructive sleep apena (OSA) is characterized by periods of reduction or complete cessation of airflow during sleep. However, the flow-sound relationship is highly variable among OSA and non-OSA individuals; it also changes for the same person at different body postures and during wake and sleep. In this study we recorded respiratory sound and flow from 93 non-OSA individuals as well as 13 OSA patients during sleep and wake. We investigated the statistical correlation between the flow-sound model parameters and anthropometric features in the non-OSA group. The results have shown that gender, height and smoking are the most significant factors that affect the model parameters. We compared the flow-sound relationship in OSA and non-OSA groups in the sitting position while awake. We also examined the variations in the model parameters in OSA patients during sleep and wake in the recumbent position. The results show that the model parameters are different in the two groups even when accounted for height, gender and position. In OSA group, the model parameters change from wake to sleep, even at the same position. The variations in the model parameters can be used to investigate the characteristics of upper airways and examine the factors that can lead to the upper airways obstruction during sleep.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".