Prophylactic mirtazapine may help to prevent post-stroke depression in people with good cognitive function
Bibliographic record
Abstract
Niedermaier N, Bohrer E, Schulte K, et al . Prevention and treatment of poststroke depression with mirtazapine in patients with acute stroke. J Clin Psychiatry 2005;65:1619–23.[OpenUrl][1] Q Does treatment with mirtazapine after an ischaemic stroke prevent onset of depression? ### ![Graphic][2]</img>Design: Randomised controlled trial. ### ![Graphic][3]</img>Allocation: Not reported. ### ![Graphic][4]</img>Blinding: Not blinded. ### ![Graphic][5]</img>Follow up period: 360 days. ### ![Graphic][6]</img>Setting: Stroke unit in academic medical centre in Ludwigshafen, Germany. ### ![Graphic][7]</img>Patients: Seventy people who had suffered an ischaemic stroke, confirmed by MRI or CT scan. People were excluded if they were currently using antidepressants, were depressed in the two weeks before stroke, were less than 18 years old, pregnant or breastfeeding, or had dysphasia that would interfere with psychiatric testing. ### ![Graphic][8]</img>Intervention: Treatment was 30 mg of mirtazapine once daily at bedtime and … [1]: {openurl}?query=rft.jtitle%253DJ%2BClin%2BPsychiatry%26rft.volume%253D65%26rft.spage%253D1619%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /embed/inline-graphic-1.gif [3]: /embed/inline-graphic-2.gif [4]: /embed/inline-graphic-3.gif [5]: /embed/inline-graphic-4.gif [6]: /embed/inline-graphic-5.gif [7]: /embed/inline-graphic-6.gif [8]: /embed/inline-graphic-7.gif
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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 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".