The SNRI venlafaxine improves emotional unawareness in patients with post‐stroke depression
Bibliographic record
Abstract
OBJECTIVE: Patients with stroke have a high prevalence of depression and unawareness of emotions or alexithymia. Here we investigated the effects of the serotoninergic and noradrenergic reuptake inhibitor (SNRI) venlafaxine in comparison with the SSRI fluoxetine on alexithymia severity in patients with DSM-IV post-stroke major depressive-like episode (PSD). METHODS: Fifty inpatients with first-ever stroke and PSD were consecutively enrolled in this randomized open-label study. Twenty-five were treated with the SNRI venlafaxine SR (75-150 mg/die), and 25 with the SSRI fluoxetine (20-40 mg/die). All patients were assessed at day 0, and after 1, 2, 4, 6, and 8 weeks, using the Mini-Mental State Examination, the Hamilton Depression Rating Scale, and the Toronto Alexithymia Scale (TAS-20). RESULTS: Patients treated with fluoxetine and those treated with venlafaxine showed similar improvement in depressive symptoms. However, patients treated with venlafaxine had a greater improvement on alexithymia severity than those treated with fluoxetine. The effect of venlafaxine on unawareness of emotions was evident in patients with alexithymia (TAS-20 >or= 61) at the baseline and in those without alexithymia (TAS-20 < 61). CONCLUSIONS: Antidepressants acting on both the serotoninergic and noradrenergic systems might represent a valid resource not only for the treatment of depression but also for improving emotional unawareness in stroke 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) | 0.001 | 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".