Mirtazapine, a sedating antidepressant, and improved driving safety in patients with major depressive disorder: a prospective, randomized trial of 28 patients.
Bibliographic record
Abstract
OBJECTIVE: The objectives of the study were to investigate the effects of mirtazapine, a sedating antidepressant, on driving safety in major depressive disorder (MDD) patients and to observe the effect of mirtazapine on daytime alertness. METHOD: Twenty-eight patients who met the DSM-IV criteria for MDD completed the study in a university teaching hospital. Half of these patients took mirtazapine 30 mg at bedtime for 30 days. A computerized driving simulator test (DST) and the Maintenance of Wakefulness Test (MWT) were conducted at baseline and on days 2, 9, 16, and 30 after commencement of antidepressant use. Fourteen untreated depressed patients performed a DST and MWT at baseline and on days 2 and 9 to evaluate the possibility of a learning effect. Data collection was from June 2005 through January 2006. RESULTS: There were significant linear effects of the treatment on road position at All Trials (p = .018) and on the morning sessions at 10:00 a.m. (p < .001) and 12:00 p.m. (p = .022) and on the number of crashes at All Trials (p = .034) and the 4:00 p.m. session (p = .050) for the group on active treatment. Compared with the values at baseline, those of road position at 10:00 a.m. significantly improved on days 2 (p < .05), 9 (p < .01), 16 (p < .01) and 30 (p < .01) and road position at 12:00 p.m. significantly improved on days 16 (p < .05) and 30 (p < .05). The number of crashes significantly decreased on day 30 (p < .05). The untreated patients showed no improvement in performance in any of the measures, suggesting that the results are not due to a learning effect. CONCLUSION: A sedating antidepressant can increase driving safety in MDD patients. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00385437.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| 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) | 0.000 | 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 teacher head, 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".