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] Design: Randomised controlled trial. ### ![Graphic][3] Allocation: Not reported. ### ![Graphic][4] Blinding: Not blinded. ### ![Graphic][5] Follow up period: 360 days. ### ![Graphic][6] Setting: Stroke unit in academic medical centre in Ludwigshafen, Germany. ### ![Graphic][7] 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] 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
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".