Is stroke unit care portable? A systematic review of the clinical trials
Bibliographic record
Abstract
BACKGROUND: It is not known if mobile stroke teams can achieve the good results seen in trials of geographically discrete stroke wards (stroke units). OBJECTIVE: To establish the effectiveness of mobile stroke teams. DESIGN: Systematic review of controlled clinical trials that compared peripatetic systems of organised stroke care (stroke team care) with alternative hospital services. METHODS: Systematic review and meta-analysis (using Cochrane Collaboration methodology and involving the primary trialists). Clinical outcomes included death, dependency, the need for institutional care and measures of the process of care such as the delivery of key investigations and treatments. RESULTS: Six clinical trials (1,085 patients) were identified; five (781 patients) compared some form of stroke team care with conventional care in general medical wards and one (304 patients) compared team care with a comprehensive stroke unit. Compared with care in general wards, stroke team care improved some aspects of the process of care, but clinical outcomes were similar. Compared with a comprehensive stroke unit, stroke team patients were significantly less likely to survive (P <0.001), return home (P < 0.001) or regain independence (P < 0.0001). Most aspects of the process of care were also poorer than in the stroke unit. CONCLUSIONS: Care from a mobile stroke team had no major impact on death, dependency or the need for institutional care.
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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 | 0.030 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".