Impact of lemborexant treatment on insomnia severity: analyses from a 12-month study of adults with insomnia disorder
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: Evaluate changes in insomnia severity in subjects with moderate to severe insomnia (Insomnia Severity Index [ISI] score ≥15) treated for 12 months nightly with lemborexant. PATIENTS/METHODS: This phase 3 randomized study comprised two 6-month treatment periods. In Period 1, 949 subjects were randomized to placebo, lemborexant 5 mg (LEM5) or 10 mg (LEM10). In Period 2, placebo subjects were rerandomized to LEM5 or LEM10; subjects initially randomized to lemborexant continued their assigned treatment. Insomnia severity was assessed using baseline ISI and 1-, 3-, 6-, 9-, and 12-month post-treatment scores. RESULTS: Mean ISI scores improved significantly across treatment groups and disease severities, with greater decreases from baseline in the LEM5 and LEM10 versus placebo groups at months 1 (-7.1, -7.2, -5.2, respectively), 3 (-8.6, -8.9, -6.1, respectively), and 6 (-9.9, -9.8, -7.2 respectively); ISI score improvements were maintained with LEM5 and LEM10 at months 9 (-11.1 and -11.2, respectively) and 12 (-11.5 and -11.2, respectively). At months 1, 3, and 6, significantly more treatment responders (≥7-point ISI score decrease from baseline) were observed with LEM5 (44%-57%) and LEM10 (44%-52%) versus placebo (30%-41%). At months 1, 3, and 6, more remitters (ISI total score <10 and < 8) were observed with LEM5 (30%-44% and 22%-34%, respectively) and LEM10 (31%-41% and 22%-31%, respectively) versus placebo (18%-28% and 11%-21%, respectively). CONCLUSIONS: Lemborexant significantly reduced insomnia severity for 12 months and increased clinically meaningful response and remission rates versus placebo. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT02952820; ClinicalTrialsRegister.eu, EudraCT Number 2015-001463-39.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".