Abstract P237: Stroke Survivors and Suicide: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: People with neurological conditions are at a higher risk of suicide compared to the general population. Despite the known association of stroke with depression and suicidal ideation, it is unclear if stroke is associated with a higher risk of suicide. Methods: We systematically searched MEDLINE, Embase, PsycINFO, and Google Scholar from their inception to July 26, 2020 using keywords and database-specific subject. We independently adjudicated and selected observational studies that compared the risk of suicide in stroke survivors to a comparison group, consisting either of people without history of stroke or the general population. We evaluated study quality using the Newcastle Ottawa scale. Using random effects meta-analysis, we calculated the pooled adjusted risk ratio (RR) of suicide in stroke survivors, and separately calculated the pooled adjusted RR of death by suicide and suicide attempt. Using prespecified analyses, we explored study-level factors to explain heterogeneity. Results: We screened 4023 articles and included 23 studies, of fair quality, totaling over 2 million stroke survivors, of whom 5563 committed suicide. Compared to the non-stroke group, the pooled adjusted RR of suicide in stroke survivors was 1.73 (95% confidence interval, 1.54-1.95, I 2 = 93%), with a significantly (P=0.03) higher adjusted risk of suicide attempt (RR 2.09, 1.69-2.58) than of death by suicide (RR 1.61, 1.44-1.80). Observed heterogeneity could not be explained by pre-specified meta-regression and subgroup analyses. Conclusions: Stroke should be recognized as an independent risk factor for suicide. Comprehensive strategies to screen and treat depression and suicidal ideation in stroke survivors should be developed to reduce the burden of suicide in stroke survivors.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".