Polysomnographic and Symptomatological Analyses of Major Depressive Disorder Patients Treated with Mirtazapine
Bibliographic record
Abstract
OBJECTIVE: This study aimed to characterize the effects of mirtazapine on polysomnographic sleep, especially slow wave sleep (SWS) and rapid eye movement (REM) sleep, as well as its effects on clinical symptoms in patients with major depressive disorder (MDD). METHOD: Sixteen MDD patients were treated with mirtazapine 30 mg taken 30 minutes before bedtime. Polysomnographic and subjective sleep, as well as other clinical data, were collected at baseline and on Days or Nights 2, 9, 16, 30, and 58 during treatment. We used repeated measures analysis of variance, including pairwise comparison, to analyze data statistically. RESULTS: Mirtazapine administration increased total SWS and the SWS in the first sleep cycle, but not SWS in the second sleep cycle. The medication increased REM latency and the duration of the first REM episode; it also decreased the number of REM episodes. Simultaneously, mirtazapine significantly reduced wake-after-sleep onset and scores on the Athens Insomnia Scale. After patients took the medication, scores on the Hamilton Depression Rating Scale-17 (HDRS-17) decreased rapidly and continuously. The changes on the Beck Depression Inventory-II were consistent with those on the HDRS-17. The medication has a tendency to increase weight. CONCLUSIONS: Mirtazapine significantly improved sleep quality, reversed sleep markers of depression, and reduced depressive symptoms in this group of MDD patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".