Association of Cardiovascular Disease and Sleep Apnea at Different Altitudes
Bibliographic record
Abstract
Abstract Otero, Liliana, Patricia Hidalgo, Rafael González, and Carlos A. Morillo. Association of cardiovascular disease and sleep apnea at different altitudes. High Alt Med Biol . 17:336–341, 2016.—We evaluated the prevalence of sleep apnea (SA) in patients with cardiovascular disease (CVD) at different altitudes. A total of 398 subjects with coronary artery disease (CAD), 144 subjects with atrial fibrillation (AF), and 292 controls (without CVD) were recruited in three cities at sea level, moderate altitude, and high altitude. All participants underwent polysomnography. Multinomial logistic regression, X 2 , and Hosmer and Lemeshow tests were used to determine interactions among CVD, SA, and altitude. Men and women with CVD at high altitude had a higher risk for SA than men and women living at lower altitudes. The highest risk of SA was observed in men with AF and men with CAD living at high altitude. Obstructive SA (OSA) prevalence was significantly increased in CVD subjects living at high altitude (OR: 5.52; p < 0.0001). Central SA (CSA) was more frequent in subjects with CVD than control group (OR: 2.44; p < 0.021). OSA was the most frequent type of SA in subjects with CVD and overweight subjects, and in control individuals with obesity or being overweight. Significant differences in the prevalence of SA associated with altitude and gender were noted in subjects with CAD and AF.
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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".