MétaCan
Menu
Back to cohort
Record W3165147153 · doi:10.1136/emermed-2020-211073

Rationale, development and implementation of the ReACanROC registry for out-of-hospital cardiac arrests in France and Canada

2021· article· en· W3165147153 on OpenAlexafffundabout
Matthieu Heidet, Hervé Hubert, Brian Grunau, Sheldon Cheskes, Valentine Baert, Laurie Fraticelli, Julie Freyssenge, Éric Lecarpentier, John M. Tallon, Karim Tazarourte, Courtney Truong, Christian Vaillancourt, Christian Vilhelm, Kosma Wysocki, Jim Christenson, Carlos El Khoury

Bibliographic record

VenueEmergency Medicine Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsOttawa HospitalUniversity of OttawaSunnybrook HospitalSt. Michael's HospitalCentre for Advancing Health OutcomesSt. Paul's HospitalUniversity of British Columbia
FundersFédération Française de CardiologieHeart and Stroke Foundation of Canada
KeywordsMedicineMedical emergencyEmergency medical servicesPopulationEnvironmental health

Abstract

fetched live from OpenAlex

France and Canada prehospital systems and care delivery in out-of-hospital cardiac arrests (OHCAs) show substantial differences. This article aims to describe the rationale, design, implementation and expected research implications of the international, population-based, France-Canada registry for OHCAs, namely ReACanROC, which is built from the merging of two nation-wide, population-based, Utstein-style prospectively implemented registries for OHCAs attended to by emergency medical services. Under the supervision of an international steering committee and research network, the ReACanROC dataset will be used to run in-depth analyses on the differences in organisational, practical and geographic predictors of survival after OHCA between France and Canada. ReACanROC is the first Europe-North America registry ever created to meet this goal. To date, it covers close to 80 million people over the two countries, and includes approximately 200 000 cases over a 10-year period.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.999

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)0.0000.000

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 head0.013
GPT teacher head0.302
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 designObservational
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

Citations5
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueEmergency Medicine JournalSame topicCardiac Arrest and ResuscitationFrench-language works237,207