Reproducibility and predictors of the apnea hypopnea index across multiple nights
Bibliographic record
Abstract
Background: Attended polysomnography (PSG) is the standard diagnostic test for sleep apnea (SA). However, due to internight variability in SA, a single night PSG may not accurately reflect the true severity of SA. Although internight variability is a well-known phenomenon, its root causes have not been fully elucidated. The objective of this study was to determine factors associated with internight variability in the apnea-hypopnea index (AHI) and its magnitude in the home environment. Methods: Each participant had a full overnight PSG simultaneous with a validated portable sleep apnea monitoring device (BresoDx®) followed by two overnight home tests using the portable monitor only. Patients were stratified into those with variable AHI and consistent AHI (AHI difference ≥10 or <10 between any 2 nights, respectively). Demographics, sleepiness, sleep test variable, and supine-predominant SA (supine-SA) were examined for any association with variable AHI. Results: Forty patients completed the protocol. The correlation between PSG and simultaneous BresoDx derived AHIs was 93.4%. Inter-class correlation between the three nights’ AHIs was 89.2%. Over two-thirds (67.5%) of patients had consistent AHIs across the three nights while 32.5% had variable AHI. AHI variability was significantly associated with supine-SA (p=0.0014) and correlated with first night’s AHI (r=0.664,p<0.001). None of the other variable, including BMI, sleepiness, gender, or test duration were associated with internight variability. Conclusion: Although portable monitoring was highly reproducible over three nights in the majority of participants, one third had a variable AHI. Supine-SA and high AHI on the first night were predictors of high internight variability.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".