Telecommunicator Cardiopulmonary Resuscitation: A Policy Statement From the American Heart Association
Bibliographic record
Abstract
Every year in the United States, >350 000 people have sudden cardiac arrest outside of a hospital environment. Sudden cardiac arrest is the unexpected loss of heart function, breathing, and consciousness and is commonly the result of an electric disturbance in the heart. Unfortunately, only ≈1 in 10 victims survives this dramatic event. Early access to 9-1-1 and early cardiopulmonary resuscitation (CPR) are the first 2 links in the chain of survival for out-of-hospital cardiac arrest. Although 9-1-1 is frequently accessed, in the majority of cases, individuals with out-of-hospital cardiac arrest do not receive lay rescuer CPR and wait for the arrival of professional emergency rescuers. Telecommunicators are the true first responders and a critical link in the cardiac arrest chain of survival. In partnership with the 9-1-1 caller, telecommunicators have the first opportunity to identify a patient in cardiac arrest and provide initial care by delivering CPR instructions while quickly dispatching emergency medical services. The telecommunicator and the caller form a unique team in which the expertise of the telecommunicator is provided just in time to a willing caller, transforming the caller into a lay rescuer delivering CPR. The telecommunicator CPR (T-CPR) process, also previously described as dispatch CPR, dispatch-assisted CPR, or telephone CPR, represents an important opportunity to improve survival from sudden cardiac arrest. Conversely, failure to provide T-CPR in this manner results in preventable harm. This statement describes the public health impact of out-of-hospital cardiac arrest, provides guidance and resources to construct and maintain a T-CPR program, outlines the minimal acceptable standards for timely and high-quality delivery of T-CPR instructions, and identifies strategies to overcome common implementation barriers to T-CPR.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.066 | 0.031 |
| Insufficient payload (model declined to judge) | 0.028 | 0.028 |
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".