Automatic Derivation of Nocturnal Desaturation Burden from Oxyhemoglobin Saturation Signal
Bibliographic record
Abstract
Sleep apnea is a common respiratory disorder which causes frequent reduction in oxygen level (hypoxemia) during sleep. Sleep apnea is highly undiagnosed due to the challenges associated with overnight polysomnography. Moreover, the measure of sleep apnea severity, called apnea-hypopnea index, is a poor measure in capturing the differences in sleep apnea related outcomes among different individuals. This is particularly important for surgical patients as sleep apnea is highly associated with postoperative respiratory depression and hypoxemia, especially during sleep. Recently, novel measures such as hypoxic burden were introduced to better quantify the burden of sleep apnea. However, calculation of hypoxic burden relies on manual annotations of respiratory events using polysomnography recordings. In this research, we present a technique which automatically detects desaturation episodes from oxyhemoglobin saturation signal and accordingly computes the burden of each desaturation episode as the area under its curve. Subsequently, we extracted features to characterize overnight desaturation burdens and investigated the association of the extracted features with sleep apnea assessment measures (apnea-hypopnea index, respiratory-related and total arousal index) and postoperative overnight respiratory depression and hypoxemia.
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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 | 0.001 |
| 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 | 0.000 | 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".