Protocol for a parallel-group, superiority randomized controlled trial of the PulsePoint mobile application to increase bystander resuscitation in out-of-hospital cardiac arrest
Bibliographic record
Abstract
Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality. Despite public awareness campaigns, widespread cardiopulmonary resuscitation (CPR) training initiatives, and deployment of public access defibrillators, potentially life-saving bystander intervention occurs inconsistently. Mobile technologies that alert nearby lay responders or off-duty professionals to OHCA events have emerged as a strategy to increase bystander CPR and AED use. The PulsePoint Randomized Controlled Trial (RCT) is a multi-centre pragmatic RCT designed to evaluate whether alerts sent via the PulsePoint Respond mobile application increase the likelihood of bystander resuscitation attempts before paramedic arrival. The trial is being conducted within a province-wide Canadian paramedic service and a municipal fire rescue service in the USA. Eligible 9-1-1 calls for suspected non-traumatic OHCA occurring in a public (non-residential) location are randomized in real time to activation or suppression of the PulsePoint system. The primary outcome is bystander CPR or AED use prior to paramedic or firefighter arrival. Patients are included in the primary analysis if they are determined to have paramedic-treated OHCA in a public location with at least one PulsePoint user within 400 m. The target sample size is 340 patients powered at 80 % to detect a 15 % absolute increase in the primary outcome. This pragmatic trial addresses a critical evidence gap in resuscitation science. We anticipate findings will inform refinement of technology implementation, policy, guideline development, and system-level decisions regarding the implementation of mobile alert systems to improve early intervention and survival from OHCA.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.009 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.203 | 0.036 |
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".