Mobile stroke unit versus standard medical care in the management of patients with acute stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Mobile stroke units have recently been introduced in the care of patients suspected of having an acute stroke, leading to shortening in the time to thrombolytics. We aimed to compare the clinical effectiveness in terms of functional outcome and survival among patients treated in mobile stroke unit and/or conventional care. Methods A systematic search of electronic databases, comparing the clinical outcomes among patients with acute stroke in the same study was conducted from 1990 to 2019. Pooled and subgroup analysis were performed using the random- and fixed-effect model based upon the I 2 heterogeneity. Results A total of 21,297 patients from 11 publications (seven randomized controlled trials and four non-randomized controlled trials including prospective cohort studies) were retrieved. This included 6065 ( n = 28.4%) of the patients treated in the mobile stroke unit and 71.6% ( n = 15,232) of the patients managed in the conventional care. The mean age at clinical presentation (70.1 ± 14.5 vs. 71.05 ± 15.8) and National Institute Health Stroke Scale (9.8 ± 1.7 vs. 8.4 ± 1.5) was comparable ( p > 0.05) in patients treated with mobile stroke unit and conventional care, respectively. The mean time-to-treatment window was significantly shorter among the patients treated in mobile stroke unit compared to conventional care (62.0 min vs. 75.0 min; p = 0.03, respectively). The pooled analysis of clinical outcome at day 7 indicated that patients treated in mobile stroke unit had 1.46-folds higher likelihood of better clinical outcome (modified Rankin scale 0–2) than those in the hospital (odds ratio: 1.46, 95% confidence interval: 1.306–2.03, p = 0.02). However, there was no significant difference in terms of mortality (odds ratio: 0.98, 95% confidence interval: 0.81–1.18, p = 0.80), stroke-related neurological deficits (odds ratio: 1.37, 95% confidence interval: 0.81–2.32, p = 0.24), and other serious adverse events (odds ratio: 0.69, 95% confidence interval: 0.39–1.20, p = 0.19) among patients treated in mobile stroke unit versus conventional care. Conclusion Our results corroborate that patients treated in mobile stroke unit lead to short-term recovery following acute stroke without influencing the mortality rate. Further prospective studies are needed to validate our results.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".