Suicidal Ideation and Insomnia in Bipolar Disorders: Idéation suicidaire et insomnie dans les troubles bipolaires
Bibliographic record
Abstract
Objective: Bipolar disorder (BD) confers elevated suicide risk and associates with misaligned circadian rhythm. Real-time monitoring of objectively measured sleep is a novel approach to detect and prevent suicidal behavior. We aimed at understanding associations between subjective insomnia and actigraphy data with severity of suicidal ideation in BDs. Methods: This prospective cohort study comprised 76 outpatients with a BD aged 18 to 65 inclusively. Main measures included 10 consecutive days of wrist actigraphy; the Athens Insomnia Scale (AIS); the Montgomery–Åsberg Depression Rating Scale (MADRS); the Quick Inventory of Depressive Symptoms-16, self-rating (QIDS-SR-16); and the Columbia Suicide Severity Rating Scale. Diagnoses, medications, and suicide attempts were obtained from chart review. Results: Suicidal ideation correlated moderately with subjective insomnia (AIS with QIDS-SR-16 item 12 ρ =0.26, P = 0.03; MADRS item 10 ρ = 0.33, P = 0.003). Graphical sleep patterns showed that suicidal patients were enriched among the most fragmented sleep patterns, and this was confirmed by correlations of suicidal ideation with actigraphy data at 2 visits. Patients with lifetime suicide attempts ( n = 8) had more varied objective sleep (a higher standard deviation of center of daily inactivity [0.64 vs. 0.26, P = 0.01], consolidation of daily inactivity [0.18 vs. 0.10, P = <0.001], sleep offset [3.02 hours vs. 1.90 hours, P = <0.001], and total sleep [105 vs. 69 minutes, P = 0.02], and a lower consolidation of daily inactivity [0.65 vs. 0.79, P = 0.03]). Conclusions: Subjective insomnia, a nonstigmatized symptom, can complement suicidality screens. Longer follow-ups and larger samples are warranted to understand whether real-time sleep monitoring predicts suicidal ideation in patient subgroups or individually.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".