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Record W3014012673 · doi:10.1177/1747493020915135

Utstein recommendation for emergency stroke care

2020· article· en· W3014012673 on OpenAlexaff
Anthony Rudd, Christopher Bladin, Pierre Carli, Deidre Anne De Silva, Thalia S. Field, EC Jauch, Peter J. Kudenchuk, MW Kurz, Tore Laerdal, MEH Ong, Peter D. Panagos, Annemarei Ranta, Christine Rutan, Michael R. Sayre, Lisbet Schönau, SD Shin, David Waters, Freddy Lippert

Bibliographic record

VenueInternational Journal of Stroke · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineStroke (engine)Medical emergencyEmergency medical servicesEmergency medicineIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Recent advances in treatment for stroke give new possibilities for optimizing outcomes. To deliver these prehospital care needs to become more efficient. AIM: To develop a framework to support improved delivery of prehospital care. The recommendations are aimed at clinicians involved in prehospital and emergency health systems who will often not be stroke specialists but need clear guidance as to how to develop and deliver safe and effective care for acute stroke patients. METHODS: Building on the successful implementation program from the Global Resuscitation Alliance and the Resuscitation Academy, the Utstein methodology was used to define a generic chain of survival for Emergency Stroke Care by assembling international expertise in Stroke and Emergency Medical Services (EMS). Ten programs were identified for Acute Stroke Care to improve survival and outcomes, with recommendations for implementation of best practice. CONCLUSIONS: Efficient prehospital systems for acute stroke will be improved through public awareness, optimized prehospital triage and timely diagnostics, and quick and equitable access to acute treatments. Documentation, use of metrics and transparency will help to build a culture of excellence and accountability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations45
Published2020
Admission routes1
Has abstractyes

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