Stroke Survivors Have Almost Three Times Higher Risk of Depression; A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Post-stroke depression (PSD) is one of the most frequent and important complications following stroke that adversely conditions functional recovery and patient’s quality of life. Meanwhile the prevalence proportion of PSD has been widely documented, ranging from 20 till 60%, the relationship between stroke and the manifestation of PSD, quantified with the odds ratio (OR), has been less explored. The primary aim of this meta-analysis is to determine prevalence odds ratio of suffering depression in stroke survivors. Prevalence proportion of PSD was also analyzed as a secondary aim. Methods: A pre-registered meta-analysis designed based on Prisma guidelines with searches from inception to 2024 September 23 was carried out on PubMed and Web of Science databases. Studies reporting the prevalence OR associated with PSD manifestation were eligible for inclusion to achieve the primary aim. Twenty-four comparative studies, including a total population of 947617 people, met the inclusion criteria. PSD prevalence proportion was extracted from 193 articles, including 484846 stroke patients. Quality assessments were performed using the Newcastle-Ottawa scale (NOS). Data were meta-analyzed using random effects model. Results: Compared with control population, stroke survivals had higher odds of developing PSD (OR: 2.81; 95% CI: 2.36-3.35). Prevalence of PSD was 32.15% ± 15.81. Conclusions: Stroke survivals have almost 3 times higher probability of suffering depression after stroke than the general population and almost one third of stroke patients will suffer PSD.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".