Reducing Barriers for Implementation of Bystander-Initiated Cardiopulmonary Resuscitation
Bibliographic record
Abstract
S udden cardiac arrest (SCA) is a leading cause of death in the United States and Canada.In the United States, each year Ϸ330 000 people die of coronary heart disease out of the hospital or in emergency departments.Of these, Ͼ150 000 SCAs occur out of the hospital.1,2 Despite the development of electrical defibrillation and the more recent implementation of lay rescuer defibrillation programs, the vast majority of these victims do not leave the hospital alive.In studies over the past 15 years, only 1.4% of patients with out-of-hospital arrest in Los Angeles, Calif, survived to hospital discharge 3 ; in Chicago, Ill, the number was 2%, 4 and in Detroit, Mich, it was Ͻ1%. 5 Conversely, a few municipalities such as Seattle, Wash, report much higher survival rates from SCA-more than 15% in 1 study 6 -which suggests that survival rates need not remain so low.Recent work in Europe and elsewhere has confirmed that a higher survival-to-hospital discharge rate is indeed a realistic goal, with survival rates as high as 9% reported in Amsterdam 7 and 21% in Maribor, Slovenia.8 The American Heart Association (AHA) uses 4 links in the "chain of survival" to illustrate the time-sensitive actions required for victims of SCA: (1) early recognition of the emergency and activation of emergency medical services (EMS), (2) early bystander cardiopulmonary resuscitation (CPR), (3) early delivery of shock(s) from a defibrillator if indicated, and (4) early advanced life support and postresuscitation care.Immediate bystander recognition of the emergency and EMS activation are critical.In many communities, however, these actions may be followed by significant delays, The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel.Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest.
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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 | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".