P.050 Effect of lemborexant on sleep architecture in adult and elderly subjects with mild to severe obstructive sleep apnea
Bibliographic record
Abstract
Background: Obstructive sleep apnea (OSA) may be associated with sleep difficulties and decreased rapid eye movement (REM) sleep. Lemborexant (LEM), a dual-orexin-receptor-antagonist approved to treat adults with insomnia, increases total sleep time (TST) and REM sleep, and demonstrated respiratory safety in subjects with mild through severe OSA. Sleep architecture was thus analyzed after LEM treatment in those subjects. Methods: Studies E2006-A001-102 and E2006-A001-113 enrolled adults with mild (apnea-hypopnea index [AHI] ≥5 – <15) or moderate (AHI ≥15 – <30)/severe (AHI ≥30) OSA without insomnia. Subjects received LEM 10mg (LEM10) or placebo (PBO) in 2 treatment periods, Days 1 (D1) and 8 (D8), separated by ≥14 days. Least-squares-mean (minutes) for each sleep stage was compared. Treatment-emergent adverse events (TEAEs) were recorded. Results: Thirty-nine subjects with mild and 33 with moderate/severe OSA were randomized. On both days, TST was significantly higher in the LEM period for these subjects. Total non-REM on D1 in subjects with mild OSA and on both days in subjects with moderate/severe OSA were higher with LEM than PBO; REM also significantly increased in subjects with mild and moderate/severe OSA. Most TEAEs were mild. Conclusions: In OSA subjects without insomnia, LEM was associated with higher TST, non-REM, and REM versus PBO.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".