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Record W2126056449 · doi:10.1093/ageing/afi038

Is stroke unit care portable? A systematic review of the clinical trials

2005· review· en· W2126056449 on OpenAlexaff
Peter Langhorne, Paola Dey, Miriam Woodman, Lalit Kalra, Sharon Wood-Dauphinée, Nikhil Patel, E Hamrin

Bibliographic record

VenueAge and Ageing · 2005
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsStroke (engine)MedicineClinical trialMEDLINEIntensive care medicineMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.481
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.199
GPT teacher head0.463
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations60
Published2005
Admission routes1
Has abstractyes

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