Effects of delay to stroke unit admission in patients with ischemic and hemorrhagic stroke
Bibliographic record
Abstract
ABSTRACT: Objective: To determine the association between delay in transfer to a central stroke unit from peripheral institutions and outcomes. Methods: We conducted a retrospective cohort study of all patients with acute stroke, admitted to a comprehensive stroke center (CSC) from three emergency departments (EDs), between 2016 and 2018. The primary outcomes were length of stay, functional status at 3 months, discharge destination, and time to stroke investigations. Results: One thousand four hundred thirty-five patients were included, with a mean age of 72.9 years, and 92.4% ischemic stroke; 663 (46.2%) patients were female. Each additional day of delay was associated with 2.0 days of increase in length of stay (95% confidence interval [CI] 0.8–3.2, p = 0.001), 11.5 h of delay to vascular imaging (95% CI 9.6–13.4, p < 0.0001), 24.2 h of delay to Holter monitoring (95% CI 7.9–40.6, p = 0.004), and reduced odds of nondisabled functional status at 3 months (odds ratio 0.98, 95% CI 0.96–1.00, p = 0.01). Factors affecting delay included stroke onset within 6 h of ED arrival (605.9 min decrease in delay, 95% CI 407.9–803.9, p < 0.0001), delay to brain imaging (59.4 min increase in delay for each additional hour, 95% CI 48.0–71.4, p < 0.0001), admission from an alternative service (3918.7 min increase in delay, 95% CI 3621.2–4079.9, p < 0.0001), and transfer from a primary stroke center (PSC; 740.2 min increase in delay, 95% CI 456.2–1019.9, p < 0.0001). Conclusion: Delay to stroke unit admission in a system involving transfer from PSCs to a CSC was associated with longer hospital stay and poorer functional 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".