B.5 Does major depression after stroke influence the risk of suicide after stroke?
Bibliographic record
Abstract
Background: Stroke survivors have a higher risk of depression and suicide, but how hospitalization for major depression modifies the risk of suicide after stroke is not well-known. Methods: We conducted a population-based matched cohort study of adults hospitalized with first-ever stroke between 2008 to 2017 matched 1:1 to the general Ontario population on age, sex, neighbourhood-level income, rurality, and comorbidities. Patients with major depression or deliberate self-harm prior to index event were excluded from both groups. We used cause-specific proportional hazards models to evaluate the association between stroke and suicide (defined as self-harm or death by suicide) and used an interaction term to assess effect modification of depression on stroke-suicide association. Results: We included 64,719 matched pairs of patients with stroke and without (45.5% female, mean age 71.4 years). Compared to matched controls, stroke survivors had a higher rate of suicide (11.1 vs. 3.2, HR 2.87 [2.35-3.51]). Depression was associated with a higher rate of suicide in both groups (HR 13.8 [8.82-21.61]). The interaction between stroke and depression was not significant (Pstroke*depression = 0.51). Conclusions: Hospitalization for depression does not modify the rate of suicide after stroke, suggesting the need to better understand the pathways leading to suicide after stroke.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".