RESCUER mobile app to support pediatric resuscitation: Study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: Pediatric out-of-hospital cardiac arrest is a leading cause of death in children. This paper describes the study protocol for the randomized control trial to test a linear cognitive aid app (RESCUER) designed to support Emergency Medical Services (EMS) clinicians in responding to neonatal and pediatric out-of-hospital cardiac arrest (POHCA). Objective: This randomized controlled trial (RCT) will investigate the effects of the RESCUER app compared with existing EMS practices and tools during simulated POHCAs. Study design: This RCT will be conducted with EMS first responders from rural and urban EMS agencies in the United States (US). EMS teams will be randomized to respond to simulated neonatal and POHCAs using either (1) the RESCUER app or (2) their current standard of care and tools. In addition to randomized assignment to intervention and control, we also will randomize the order of simulation scenarios. Main outcome measures: . Conclusion: We hypothesize that the app will decrease time to complete AHA recommended steps for NRP and PALS, compared to EMS teams' current standard of care and tools. This study will examine whether an app used by EMS teams in responding to simulated neonatal and pediatric OHCAs will improve resuscitation performance and decrease cognitive load. Trial Registration Number: NCT06768099 (ClinicalTrials.gov).
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.114 | 0.014 |
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".