Abstract WP296: Poststroke Dementia is Associated With Early Institutionalization
Bibliographic record
Abstract
Objective: To investigate whether poststroke dementia diagnosed after ischemic stroke is associated with earlier permanent institutionalization. Methods: We included 410 consecutive patients aged 55 to 85 years with ischemic stroke admitted to the Helsinki University Central Hospital (The SAM cohort) with a 21-year follow-up. Hospitalization and nursing home admissions were reviewed from national registers. Dementia was diagnosed using the Diagnostic and Statistical Manual of Mental Disorders 3rd edition (DSM-III) criteria. Kaplan-Meier plots log-rank and binary logistic regression (odds ratio, OR) and Cox multivariable proportional hazards model were used to study the association of poststroke dementia status and time of permanent institutionalization. HR and OR with their 95% confidence intervals (CI) are reported. Results: Poststroke dementia was associated with more frequent (OR 2.05; CI 1.29-3.24) permanent institutionalization (Mantel-Haenszel), and earlier permanent institutionalization (5.6 versus 13.6 years; log-rank p<0.001). After adjusting for significant covariates from univariable analyses, poststroke dementia was associated with increased hazards ratio of permanent institutionalization during 21 years of follow-up (1.52; CI 1.05-2.19), see Table. Conclusions: Poststroke dementia at 3 month after stroke is associated with earlier permanent institutionalization compared with stroke patient without poststroke dementia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".