Stroke in the Very Elderly: Hospital Care, Case Fatality and Disposition
Bibliographic record
Abstract
BACKGROUND: The worldwide growing number of older people represents a new phenomenon. Considering that the prevalence of stroke increases with age and higher life expectancy, the prevalence of stroke will likely rise in the next decade. However, limited information is available about the burden of stroke in individuals over 90. METHODS: This is a subgroup analysis from a multicenter cohort study including individuals admitted with an ischemic stroke to a broad range of hospitals across Canada. Patients were identified from the Canadian Hospital Morbidity database (HMDB), which is a national database that contains patient-level sociodemographic, diagnostic and administrative information. Multivariable analysis was performed using logistic regression. Outcomes measures include risk-adjusted stroke fatality, ICU admissions, medical complications, length of hospital stay and discharge disposition. RESULTS: Among 26,676 patients with ischemic stroke admitted to 606 hospitals, 2,015 (7.6%) were aged 90 years or older. Risk-adjusted fatality at discharge was 6.3% (age <69), 12.5% (age 70-79), 22.0% (age 80-89) and 36.1% (age >or=90) (p < 0.001). Patients aged 90 and over were more likely admitted on weekends (28.1 vs. 24.6; p < 0.001), and less likely to be admitted to the ICU (4.3 vs. 13.0%, p < 0.001) and discharged to their pre-stroke residence (39.9% for those over 90 vs. 57.3% for patients younger than 90, p < 0.001). In the multivariable analysis, nonagenarians and older were 5-8 times more likely to die after adjusting for covariates. CONCLUSION: In our study, stroke patients over 90 had higher risk-adjusted mortality, longer hospitalization, and were less likely to be discharged to their original place of residence. In view of these findings, strategies need to be implemented to facilitate equal access to specialized stroke care for the elderly.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".