Impact of a Stroke Unit on Length of Hospital Stay and In-Hospital Case Fatality
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Randomized trials have demonstrated reduced morbidity and mortality with stroke unit care; however, the effect on length of stay, and hence the economic benefit, is less well-defined. In 2001, a multidisciplinary stroke unit was opened at our institution. We observed whether a stroke unit reduces length of stay and in-hospital case fatality when compared to admission to a general neurology/medical ward. METHODS: A retrospective study of 2 cohorts in the Foothills Medical Center in Calgary was conducted using administrative databases. We compared a cohort of stroke patients managed on general neurology/medical wards before 2001, with a similar cohort of stroke patients managed on a stroke unit after 2003. The length of stay was dichotomized after being centered to 7 days and the Charlson Index was dichotomized for analysis. Multivariable logistic regression was used to compare the length of stay and case fatality in 2 cohorts, adjusted for age, gender, and patient comorbid conditions defined by the Charlson Index. RESULTS: Average length of stay for patients on a stroke unit (n=2461) was 15 days vs 19 days for patients managed on general neurology/medical wards (n=1567). The proportion of patients with length of stay >7 days on general neurology/medical wards was 53.8% vs 44.4% on the stroke unit (difference 9.4%; P<0.0001). The adjusted odds of a length of stay >7 days was reduced by 30% (P<0.0001) on a stroke unit compared to general neurology/medical wards. Overall in-hospital case fatality was reduced by 4.5% with stroke unit care. CONCLUSIONS: We observed a reduced length of stay and reduced in-hospital case-fatality in a stroke unit compared to general neurology/medical wards.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".