The Effect of Donepezil on Arousal Threshold and Apnea–Hypopnea Index. A Randomized, Double-Blind, Cross-Over Study
Bibliographic record
Abstract
Abstract Rationale Obstructive sleep apnea (OSA) has multiple pathophysiological causes. A low respiratory arousal threshold (ArTh) and a high loop gain (unstable ventilatory control) can contribute to recurrent respiratory events in patients with OSA. Prior studies have shown that donepezil, an acetylcholinesterase inhibitor, might improve OSA, but the mechanism is unknown. Objectives To determine whether a single dose of donepezil lowers the apnea–hypopnea index by modulating the ArTh or loop gain. Methods In this randomized, double-blind, crossover trial, 41 subjects with OSA underwent two polysomnograms with ArTh and loop gain evaluated, during which 10 mg of donepezil or placebo was administered. Measurements and Main Results: Compared with placebo, sleep efficiency (77.2 vs. 71.9%; P = 0.015) and total sleep time decreased with donepezil (372 vs. 351 min; P = 0.004). No differences were found in apnea–hypopnea index (51.8 vs. 50.0 events/h; P = 0.576) or nadir oxygen saturation as determined by pulse oximetry (80.3 vs. 81.1%; P = 0.241) between placebo and donepezil, respectively. ArTh was not significantly changed (–18.9 vs. –18.0 cm H2O; P = 0.394) with donepezil. As a whole group, loop gain (ventilatory response to a 1-cycle/min disturbance) did not change significantly (P = 0.089). Conclusions A single dose of donepezil did not appear to affect the overall severity of OSA in this patient group, and no consistent effects on ArTh or loop gain were observed. Donepezil may have minor effects on sleep architecture. Clinical trial registered with www.clinicaltrials.gov (NCT02264353).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".