Supplementary Material for: A Systematic Review and Meta-Analysis Comparing Mortality Between In-Hospital vs Community-Onset Acute Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose: Patients who experience in-hospital strokes may suffer from delays in stroke recognition, delays to acute treatment and management. We aimed to assess evidence for the difference in mortality between patients with in-hospital stroke and those with community-onset stroke. Methods: We searched MEDLINE, EMBASE and SCOPUS (from inception to October 8, 2024) to identify studies comparing mortality outcomes for in-hospital and community-onset stroke patients. We collected data on study characteristics, summarized the quality of evidence, evaluated risk of bias of studies using the Newcastle Ottawa Scale, and investigated clinical sources of heterogeneity. We performed a random-effects meta-analysis to estimate the pooled odds of mortality of in-hospital stroke vs community-onset stroke patients. Results: Forty-one studies, collectively with 3,038,211 patients and, of whom, 3% experienced in-hospital stroke were included in the review. In-hospital stroke patients had an approximate 2.3-fold higher odds of in-hospital mortality (pooled OR 2.27; 95% CI 1.80 – 2.86; 32 patient cohorts) and 1.9-fold higher odds of 3-month mortality (pooled OR 1.87; 95% CI 1.43–2.45; 14 patient cohorts) compared to community-onset stroke patients. Meta-analyses stratified by acute treatment received and study characteristics revealed consistently higher odds of death among in-hospital stroke patients compared to community-onset stroke patients. Acute treatment received, study setting, geographic region and components of study quality were significant sources of heterogeneity. Most concerns in study quality were due to potential risks of confounding. Conclusion: There was a consistently higher odds of in-hospital and 3-month mortality among in-hospital acute ischemic stroke patients compared to their community-onset counterparts, highlighting the need for targeted interventions to reduce this disparity.
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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 | 0.005 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.700 | 0.037 |
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".