Population-based study of home-time by stroke type and correlation with modified Rankin score
Bibliographic record
Abstract
OBJECTIVE: To describe home-time, stratified by stroke type, in a complete population and to determine its correlation with modified Rankin Scale (mRS) scores. METHODS: We used linked administrative data to derive home-time in all patients admitted for a cerebrovascular event in Alberta, Canada, between 2012 and 2016. Home-time is the number of days spent outside a health institution in the first 90 days after index hospitalization. We used negative binomial regression, adjusted for age, sex, Charlson comorbidity index, and hospital location, to determine the association between home-time and stroke type. In 552 patients enrolled in 4 acute ischemic stroke clinical trials, we used multivariable ordinal logistic regression analysis to determine the association between home-time and mRS score at 90 days. RESULTS: Among 15,644 patients (n = 10,428 with ischemic stroke, n = 1,415 with intracerebral hemorrhage, n = 760 with subarachnoid hemorrhage, n = 3,041 with TIA), patients with TIA have the longest home-time, almost triple the number of days at home compared to patients with intracerebral hemorrhage (incidence rate ratio 2.85, 95% confidence interval [CI] 2.58-3.15). Among clinical trial ischemic stroke patients, longer home-time was associated with a lower mRS score at 90 days (adjusted common odds ratio 1.04, 95% CI 1.04-1.05). CONCLUSIONS: We showed that home-time is an objective and graded indicator that is correlated with disability after stroke. It is obtainable from administrative data, applicable to different stroke types, and a valuable outcome indicator in population-based health services research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".