Effect of Depression on Stroke Morbidity and Mortality
Bibliographic record
Abstract
OBJECTIVES: This narrative review examines the evidence and discusses the clinical relevance of depression as a risk factor for stroke morbidity and mortality. It also proposes recommendations for future research. METHODS: We used the Medline computer database to search the relevant original studies published in English from January 1966 to December 2001. Our key words were as follows: depressive disorder, cerebrovascular disease, stroke, vascular risk factors, and mortality. Articles that investigated the relation between antecedent depression and subsequent stroke morbidity and mortality were collected and reviewed. RESULTS: Since 1990, 8 prospective studies have been published. Among these 8 studies, 6 addressed depression and stroke morbidity, 1 investigated the association of depression with stroke morbidity and stroke mortality, and 1 investigated the association with stroke mortality only. Of 7 studies examining the independent effect of depression on stroke morbidity, 6 were positive. With regard to stroke mortality, 2 studies found an independent association between depression and specific stroke mortality. The contributions and methodological limitations of these studies are discussed. CONCLUSIONS: Emerging data suggest an association between depressive symptoms and increased risk for stroke morbidity and mortality. More methodologically sound studies are needed to elucidate causal pathways that link depression and cerebrovascular disease. They are also needed to determine the effect of depression intervention on reducing the risk of cerebrovascular events. Information on author affiliations appears at the end of the article.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".