Outcomes in Hospitalized Ischemic Stroke Patients with Dementia on Admission: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Dementia prevalence is rising, and it will double in the next 20 years. This study sought to understand the prevalence of dementia in hospitalized patients with ischemic stroke, and its impact on outcomes. METHODS: Using the Canadian Institute of Health Information's (CIHI) Discharge Abstract Database (DAD), all acute ischemic stroke admissions from April 2003 to March 2015 in Canada (excluding Quebec) were analyzed. Concurrent dementia at the time of admission was assessed based on hospital diagnostic codes. Characteristics and in-hospital outcomes were compared in patients with and without dementia using χ 2 and negative binomial, as well as Poisson regression analysis. RESULTS: During the observed period, 313,138 people were admitted to a hospital in Canada for an ischemic stroke. Of those, 21,788 (7.0%) had a concurrent diagnosis of dementia. People with dementia had older median age (84 vs. 76 years; p<0.0001), were more often female (59.6% vs. 48.4%; p<0.0001) and more often had Charlson-Deyo Comorbidity Index ≥2 (64.5% vs. 43.5%; p<0.0001). Patients with dementia were less likely to be discharged to a rehabilitation facility (adjusted risk ratio [RR] 3.089, 95% confidence interval [CI] 2.992-3.188, p<0.0001) or home independently (adjusted RR 0.756, 95% CI 0.737-0.776, p<0.0001).InterpretationApproximately 1 in 13 hospitalized ischemic stroke patients has coded dementia. Patients with ischemic stroke and concurrent dementia have higher mortality, face significantly more dependence after stroke and utilize greater healthcare resources than stroke patients without dementia. Causative conclusions are limited by the administrative data source. Early care planning and coordination could potentially optimize outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".