Effects of quetiapine on sleep architecture in patients with unipolar or bipolar depression
Bibliographic record
Abstract
Laura Gedge1, Lauren Lazowski1, David Murray2, Ruzica Jokic2,3, Roumen Milev2,31Centre for Neuroscience Studies, 2Department of Psychiatry, Queen’s University, Kingston, 3Providence Care-Mental Health Services, Kingston, Ontario, CanadaObjective: To determine the effect of adjunctive quetiapine therapy on the sleep architecture of patients with bipolar or unipolar depression.Methods: This is a prospective, single-blind, repeated measures polysomnographic study. Sleep architecture was analyzed by overnight polysomnography, and subjective sleep quality was measured using the Pittsburgh Sleep Quality Index. The Hamilton Rating Scale for Depression, Montgomery Asberg Depression Rating Scale, Young Mania Rating Scale, and Clinical Global Impression-Severity Scale were employed to quantify changes in illness severity with adjunctive quetiapine treatment. Polysomnographs and clinical measures were administered at baseline, after 2–4 days of treatment, and after 21–28 days of quetiapine treatment. The average dose of quetiapine was 155 mg, ranging from 100–200 mg.Results: Adjunctive quetiapine therapy did not significantly alter sleep efficiency, sleep continuity, or Pittsburgh Sleep Quality Index scores. Respiratory Disturbance Index and percentage of total time in rapid eye movement (REM) sleep significantly decreased and the percentage of total time in non-REM sleep, and duration of Stage 2 and non-REM sleep significantly increased after 2–4 days of quetiapine treatment. Illness severity significantly decreased over time.Conclusions: Adjunctive quetiapine treatment alters sleep architecture in patients with major depressive disorder or bipolar disorder, which may partially explain its early antidepressant properties. Changes in sleep architecture are more robust and significant within two to four days of starting treatment.Keywords: quetiapine, sleep architecture, depression, bipolar disorder
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".