Bibliographic record
Abstract
OBJECTIVE: To review the world's (English-language) publications related to depression following stroke. METHOD: The databases from MEDLINE and PubMed were reviewed for articles related to poststroke depression (PSD), depression and cerebral vascular accident, depression and cerebral vascular disease, and depression and cerebral infarction. RESULTS: Most studies examined prevalence rates of depression and the clinical correlates of depression. Based on pooled data, the overall prevalence of major depression was 21.7% and minor depression was 19.5%. The strongest single correlate of depression was severity of impairment in activities of daily living. However, the existence of depression at baseline was found to be associated with greater impairment at follow-up, ranging from 6 weeks to 2 years in 83% of studies. Further, depression following acute stroke was also associated with greater cognitive impairment and increased mortality. PSD has been shown in 6 double-blind controlled studies to be effectively treated with antidepressants, and 1 study has recently shown that PSD can be effectively prevented. CONCLUSIONS: During the past 20 years, significant progress has been made in the identification and treatment of depression following stroke. In the future, antidepressant treatment will likely play an increasing role in the management of patients with acute stroke. Further research is needed to identify the mechanisms of depression and why antidepressants lead to improved physical and cognitive recovery and decreased mortality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".