P.047 Effect of lemborexant on sleep architecture in subjects with comorbid insomnia and mild obstructive sleep apnea from a phase 3 trial
Bibliographic record
Abstract
Background: Lemborexant (LEM), a dual-orexin-receptor-antagonist approved to treat adults with insomnia, increases total sleep time (TST) and rapid eye movement (REM) sleep. Patients with obstructive sleep apnea (OSA) or co-morbid insomnia and OSA (COMISA) report sleeping difficulties and reduced REM, therefore sleep architecture was analyzed during LEM treatment. Methods: Study E2006-G000-304 (NCT02783729) was a 1-month, randomized, double-blind, placebo (PBO)- and active-comparator zolpidem-ER 6.25mg (ZOL)-controlled study in adults ≥55y with insomnia disorder. Subjects received PBO, LEM 5mg (LEM5), 10mg (LEM10), or ZOL. Least-square-mean duration of each sleep stage (minutes) was compared from pooled data on Nights (NT)1/2 and NT29/30 for mild OSA subjects (apnea hypopnea index ≥5 to <15 events/h). Treatment-emergent adverse events (TEAEs) were recorded. Results: Of 409 subjects with mild OSA (LEM5=114/LEM10=105/ZOL=112/PBO=78) change from baseline (CFB) in TST and REM sleep was significantly larger with both LEM doses versus ZOL/PBO on both nights. CFB for total nonREM sleep was significantly higher (P<0.0001) with both LEM doses versus PBO on both nights. LEM5 showed significantly higher (P<0.05) nonREM sleep versus ZOL at NT29/30. Most TEAEs were mild/moderate. Conclusions: LEM significantly increased TST, REM, and nonREM sleep versus PBO in subjects with insomnia and mild OSA. Data support LEM treatment in the COMISA population.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.013 | 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".