INPATIENTS: Comparing Clinical Characteristics and Outcomes of Adults with In-Hospital and Community-Onset Strokes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In-hospital strokes comprise a small but high-risk subgroup of patients and are associated with worse outcomes compared to community-onset strokes. We compared clinical characteristics, workflow metrics and clinical outcomes of adult patients with in-hospital strokes and those with community-onset strokes in Alberta. METHODS: We conducted a retrospective cohort study (INPATIENTS: IN-hosPitAl sTrokes InAlbErta iNcidence and ouTcomeS) from Jan 1, 2018-Dec 31, 2022 using provincial administrative data and chart review to compare in-hospital and community-onset acute ischemic and hemorrhagic strokes. We performed multivariable logistic regression to determine the association of stroke onset location (in-hospital vs community-onset) with the following outcomes: in-hospital mortality, prolonged hospital stay and in-hospital complications. Negative binomial regression was conducted to compare workflow metrics between cohorts. All models were adjusted for age, sex, comorbidities, facility type and admission year. RESULTS: Among 24,039 stroke admissions, 2,545 (10.6%) were in-hospital strokes and 20,895 (86.9%) were ischemic. In-hospital strokes had higher rates of comorbidities and were associated with higher in-hospital mortality (adjusted OR [aOR] 3.09; 95% CI 2.80-3.41), prolonged hospital stays (aOR 5.47; 95% CI 4.89-6.112) and increased in-hospital complications. In-hospital ischemic stroke patients receiving thrombectomy showed lower odds of in-hospital mortality (aOR 0.46; 95% CI, 0.28-0.75) and pneumonia (aOR 0.38; 95% CI, 0.20-0.71) compared to non-treated patients. Workflow times were significantly longer in in-hospital ischemic strokes compared to community-onset strokes. DISCUSSION: Patients with in-hospital stroke experience higher rates of mortality, poorer clinical outcomes and significant delays in management. Targeted quality improvement efforts are needed to address care gaps and improve outcomes in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".