Up to 206 Million People Reached and Over 5.4 Million Trained in Cardiopulmonary Resuscitation Worldwide: The 2019 International Liaison Committee on Resuscitation World Restart a Heart Initiative
Bibliographic record
Abstract
Sudden out-of-hospital cardiac arrest is the third leading cause of death in industrialized nations. Many of these lives could be saved if bystander cardiopulmonary resuscitation rates were better. "All citizens of the world can save a life-CHECK-CALL-COMPRESS." With these words, the International Liaison Committee on Resuscitation launched the 2019 global "World Restart a Heart" initiative to increase public awareness and improve the rates of bystander cardiopulmonary resuscitation and overall survival for millions of victims of cardiac arrest globally. All participating organizations were asked to train and to report the numbers of people trained and reached. Overall, social media impact and awareness reached up to 206 million people, and >5.4 million people were trained in cardiopulmonary resuscitation worldwide in 2019. Tool kits and information packs were circulated to 194 countries worldwide. Our simple and unified global message, "CHECK-CALL-COMPRESS," will save hundreds of thousands of lives worldwide and will further enable many policy makers around the world to take immediate and sustainable action in this most important healthcare issue and initiative.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".