A systematic review of anti-suicidal effects of sedative-hypnotics and cognitive behavioral therapy for insomnia
Bibliographic record
Abstract
Abstract Suicide accounts for over 700,000 deaths per year globally and remains a public health priority. Evidence suggests that sleep-related interventions may be effective in reducing depressive symptom severity and suicidal thoughts in patients diagnosed with depression and comorbid insomnia. This study aims to systematically review the efficacy of sedative-hypnotics and/or cognitive behavioral therapy for insomnia (CBT-I) on measures of suicidality. In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, PubMed, Medline, Cochrane Library, Embase, Scopus, and Web of Science were searched from inception to July 30, 2024. Studies were included if they (1) were randomized controlled trials (RCTs) and (2) reported on suicide-related measures associated with sleep interventions as a primary outcome, secondary outcome, or a safety measure. We endeavored to define and operationalize suicidality as suicidal ideation (SI), suicide attempts (SA), and suicide completion (SC). In cases where study authors failed to separate these three dimensions, the term “suicidality” was applied. Eighteen studies were identified meeting inclusion criteria, comprised of studies investigating benzodiazepines ( n = 2), Z-drugs ( n =4), orexin receptor antagonists (ORAs) ( n =8), and CBT-I ( n =4). Zolpidem reduces SI as well as insomnia (linear association = 0.12, p <0.05) as evidenced by improvement on both the Columbia-Suicide Severity Rating Scale (C-SSRS) and the Scale for Suicide Ideation (SSI). ORAs were not associated with either an increase or decrease in suicidality. CBT-I alleviates SI in patients with insomnia ( t = −3.35, p <0.05). Effectively treating insomnia is associated with reduced SI. Available evidence suggests that Food and Drug Administration (FDA)-approved sedative-hypnotics do not increase the risk of suicidality.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".