MétaCan
Menu
Back to cohort
Record W2101025333 · doi:10.1503/cmaj.071253

Toward a more effective approach to stroke: Canadian Best Practice Recommendations for Stroke Care

2008· review· en· W2101025333 on OpenAlexaffvenueabout
Patrice Lindsay, Mark Bayley, Amy McDonald, Ian D. Graham, Grace Warner, Michael D. Hill

Bibliographic record

VenueCanadian Medical Association Journal · 2008
Typereview
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsQueen Elizabeth II Health Sciences CentreOntario Stroke NetworkCanadian Institutes of Health ResearchToronto Rehabilitation InstituteUniversity of TorontoDalhousie University
Fundersnot available
KeywordsBest practiceStroke (engine)Health careMedicineMEDLINEBest evidenceNursingMedical educationPolitical science

Abstract

fetched live from OpenAlex

Each year more than 50,000 Canadians experience a stroke and more than 300,000 currently live with its effects. Despite the evidence supporting best practices in stroke care, significant gaps in translating this knowledge into action remains in Canada. An interdisciplinary working group of the Canadian Stroke Strategy was formed to develop best-practice recommendations relevant to Canadian health care. The working group used a rigorous process to develop the guidelines, which included reviewing existing stroke recommendations and research literature, and consulting a national interprofessional consensus panel. The Canadian Best Practice Recommendations for Stroke Care consist of 24 recommendations based on the strongest evidence and address topics that span the full continuum of stroke care. Implementation and dissemination of these recommendations is in progress. Bringing about change will require political will and collaboration throughout the health care system.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.755

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.053
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0120.013
Science and technology studies0.0040.003
Scholarly communication0.0050.004
Open science0.0080.003
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0060.003

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 head0.179
GPT teacher head0.486
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations139
Published2008
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Medical Association JournalSame topicClinical practice guidelines implementationFrench-language works237,207