Relation between depression after stroke, antidepressant therapy, and functional recovery
Bibliographic record
Abstract
The aim was to evaluate the effects of poststroke depression and antidepressant therapy on the improvement of motor scores and disability, to verify if the negative effects of poststroke depression on functional recovery could be counterbalanced by taking antidepressant drugs. RESULTS OBTAINED BEFORE, DURING, AND AFTER REHABILITATION: On the Barthel index, Canadian neurological scale, and Rivermead mobility index-by 49 depressed patients with stroke, who had been treated (n=25) or not treated (n=24) according to the different therapeutic approaches of their physicians, were compared with results similarly obtained by 15 non-depressed patients with stroke. Analysis was by multivariate analysis of variance for repeated measures There was a non-significant difference between the groups in their motor and functional scores, and a significant improvement on time. A significant interaction between group and time was seen. This interaction was particularly significant on the Rivermead mobility index, and was due to the fact that the recovery of non-treated depressed patients with stroke was less than the non-depressed and the depressed but treated patients with stroke. Furthermore, recovery from depression was significantly greater in treated than in non-treated depressed patients with stroke. In conclusion, poststroke depression has negative effects on functional recovery, and a pharmacological treatment of depression can counterbalance this effect.
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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 | 0.001 | 0.003 |
| 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".