Contributions of Upper Airway Mechanics and Control Mechanisms to Severity of Obstructive Apnea
Bibliographic record
Abstract
The contributions of pharyngeal mechanical abnormalities, flow demand, and compensatory effectiveness to obstructive sleep apnea severity were determined in 82 patients. Flow demand was estimated from mean inspiratory flow on continuous positive airway pressure. Mechanical load on upper airway muscles was estimated from minimal effective continuous positive airway pressure, flow demand, and minimum flow observed during brief pressure dial downs. Compensatory effectiveness was estimated by relating polysomnographic severity and mechanical load. Mechanical load was more severe in men, in supine position, and in older and heavier patients. Higher flow demand contributed significantly to mechanical load in men and in those who are obese. At the same mechanical load, severity was independent of age, sex, or body mass index but was greater in the supine position and in REM sleep. Mechanical load accounted for only 34% of variability in severity. Eighty-two percent of patients experienced periods of stable breathing despite mechanical loads that would produce continuous cycling without compensation. I conclude that most patients can adequately compensate for the abnormal mechanics, at least part of the time. Higher flow demand contributes to severity in men and in obesity. Severity is largely due to factors other than mechanical load. Compensatory effectiveness is impaired in the supine position and in REM sleep, but not by age, sex, or body mass index.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".