Sustained oximetric desaturation as a cardiac risk indicator remains correlated with nonspecific sleep apnea indices.
Bibliographic record
Abstract
BACKGROUND: Sustained oximetric desaturation is a risk factor for cardiac morbidity and mortality in sleep apnea, Traditional sleep apnea indices are not as predictive. AIM: To model the effects of two indicators of sleep apnea severity on sustained oximetric desaturation. METHODS: Exploratory data analysis and an analysis of covariance (ANCOVA) were performed on an anonymized convenience sample of 5000 ambulatory sleep polygraphs. The ANCOVA modelled the effects of apnea-hypopnea index (AHI, events/h) and the percentage of recording time with inspiratory flow limitation (IFL, %) on the percentage of time with SpO2 < 90% (Under90, %). RESULTS: The medians (and interquartile ranges) for Under90, AHI, and IFL were 7.3 (0.01, 29.3)%, 12.7 (6.6, 23.6)/h, and 17.7 (6.4, 24.9)%. The sums of squares for AHI, IFL, AHI:IFL, and residuals were 52.5, 0.8, 0.3, and 332.1. The F scores and Pr(>F) for AHI, IFL, and AHI:IFL were 880.7 (p << 0.001), 13.4 (p = 0.002), and 5.6 (p=0.018). CONCLUSIONS: The time with SpO2 < 90%, a risk factor for cardiac morbidity and mortality, strongly correlated with AHI and less so with IFL, two insensitive markers for cardiac morbidity and mortality. Further modelling including epidemiological risk scores such as the Framingham score and other polysomnogram risk factors such as pulse wave amplitude variation may improve cardiac risk prediction with sleep apnea.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".