Using tracheal breathing sounds and anthropometric information for screening obstructive sleep apnoea during wakefulness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Obstructive sleep apnoea (OSA) is a common yet underdiagnosed disorder. Undiagnosed OSA significantly increases perioperative morbidity and mortality for OSA patients undergoing surgery, requiring full anaesthesia. Tracheal breathing sounds characteristics during wakefulness have shown a high correlation with the apnoea-hypopnea index (AHI), while they are also affected by the anthropometric parameters, e.g., sex, age, etc. This study investigates the effects of the anthropometric parameters on our new quick objective OSA screening tool during wakefulness. Breathing sounds of 122 individuals (71 with AHI <15 as non-OSA and 51 with AHI > 15 as OSA) were recorded during wakefulness in the supine position. The spectra and bi-spectra of 81 (47 non-OSA) individuals' signals, which were randomly selected, were analysed as a training dataset to extract the most significant features with the lowest sensitivity to the anthropometric parameters. Using a support vector machine (SVM) classifier, these features resulted in 72.1, 64.7 and 77.5% testing classification accuracy, sensitivity and specificity, respectively. We also investigated classifying subjects into subgroups related to each anthropometric parameter and incorporating a voting procedure. This routine resulted in 83.6, 74.5 and 90.1% testing classification accuracy, sensitivity and specificity, respectively. In conclusion, it is possible to positively utilise the anthropometric information to enhance the classification accuracy for a reliable OSA screening procedure during wakefulness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it