All Obstructive Sleep Apnea Events Are Not Created Equal: The Relationship Between Event-Related Hypoxemia and Physiologic Response
Bibliographic record
Abstract
Abstract Rationale Obstructive sleep apnea (OSA) severity is typically assessed by the apnea–hypopnea index (AHI), a frequency-based metric that allocates equal weight to all respiratory events. However, more severe events may have a greater physiologic impact. Objectives The purpose of this study was to determine whether the degree of event-related hypoxemia would be associated with the postevent physiologic response. Methods Patients with OSA (AHI, ⩾5/h) from the multicenter Canadian Sleep and Circadian Network cohort were studied. Using mixed-effect linear regression, we examined associations between event-related hypoxic burden (HBev) assessed by the area under the event-related oxygen saturation recording with heart rate changes (ΔHRev), vasoconstriction (vasoconstriction burden [VCBev] assessed with photoplethysmography), and electroencephalographic responses (power ratio before and after events). Results Polysomnographic recordings from 658 patients (median [interquartile range] age, 55.00 [45.00, 64.00] yr; AHI, 27.15 [14.90, 64.05] events/h; 42% female) were included in the analyses. HBev was associated with an increase in all physiologic responses after controlling for age, sex, body mass index, sleep stage, total sleep time, and study centers; for example, 1 standard deviation increase in HBev was associated with 0.21 [95% confidence interval, 0.2, 0.22], 0.08 [0.08, 0.09], and 0.22 [0.21, 0.23] standard deviation increases in ΔHRev, VCBev, and β-power ratio, respectively. Conclusions Increased event-related hypoxic burden was associated with greater responses across a broad range of physiologic signals. Future metrics that incorporate information about the variability of these physiologic responses may have promise in providing a more nuanced assessment of OSA severity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".