ReACanROC: Towards the creation of a France–Canada research network for out-of-hospital cardiac arrest
Bibliographic record
Abstract
AIMS: There are large differences between emergency medical systems, which may account for variability in outcomes. We seek to compare prehospital organizations, response modes, patient characteristics and outcomes after out-of-hospital cardiac arrest, between France and Canada, and discuss the need for the first European-North American prehospital research network on out-of-hospital cardiac arrest. METHODS: Preliminary comparative description of data drawn from two nation-wide, population-based, Utstein-style prospectively implemented registries for out-of-hospital cardiac arrest in France and Canada (France: RéAC, Canada: CanROC), covering approximately 80 million people, and soon to be participating in an international research network in 2020. RESULTS: Since creation, 103,722 cases were included in France and approximately 99,317 in Canada. Data used in this work were drawn from 2011 to 2016, and comprised around 33,688 adult, non-traumatic, treated cases in Canada, and 55,358 in France, leading to estimated incidence rates of 75.3/100,000 inhabitants in France and 83/100,000 in Canada. In both countries, out-of-hospital cardiac arrest predominantly occurred in male patients, in their late sixties, at home, of presumed cardiac aetiology. Bystander cardiopulmonary resuscitation was provided in half of the cases. First assessed cardiac rhythm was shockable in 16% (France) vs. 22% (Canada). Professional resuscitation was attempted in 82% (France) and 60% (Canada). Prehospital organizations and response modes differed in the constitution of responding teams (France: physician-led advanced life support, Canada: trained paramedics), in response time intervals (call to first professional responders' arrival at scene 6.5 min (interquartile range IQR [5.2-8.3]) (Canada) vs. 10 min [7-15] (France)), in on-scene interventions, type of referral at hospital (France: systematic bypass of emergency department, tertiary hospital first, Canada: occasional bypass, mainly closest hospital first), and in outcomes (overall survival at hospital discharge in France: 5% vs. Canada: 11%). CONCLUSION: Despite similarities in some out-of-hospital cardiac arrest Utstein variables, several differences exist between French and Canadian prehospital systems, and ultimately, between outcomes. The creation of the ReACanROC research network will facilitate the conduction of further analyses to better understand predictors of this variability.
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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 | 0.045 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".