PediAppRREST: effectiveness of an interactive cognitive support tablet app in reducing deviations from guidelines in the management of paediatric cardiac arrest: protocol for a simulation-based randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Paediatric cardiac arrest (PCA), despite its low incidence, has a high mortality. Its management is complex and deviations from guideline recommendations occur frequently. We developed a new interactive tablet app, named PediAppRREST, to support the management of PCA. The app received a good usability evaluation in a previous pilot trial. The aim of the study is to evaluate the effectiveness of the PediAppRREST app in reducing deviations from guideline recommendations in PCA management. METHODS AND ANALYSIS: This is a multicentre, simulation-based, randomised controlled, three-parallel-arm study. Participants are residents in Paediatric, Emergency Medicine, and Anaesthesiology programmes in Italy. All 105 teams (315 participants) manage the same scenario of in-hospital PCA. Teams are randomised by the study statistician into one of three study arms for the management of the PCA scenario: (1) an intervention group using the PediAppRREST app or (2) a control group Paediatric Advanced Life Support (CtrlPALS+) using the PALS pocket reference card; or (3) a control group (CtrlPALS-) not allowed to use any PALS-related cognitive aid. The primary outcome of the study is the number of deviations (delays and errors) in PCA management from PALS guideline recommendations, according to a novel checklist, named c-DEV15plus. The c-DEV15plus scores will be compared between groups with a one-way analysis of variance model, followed by the Tukey-Kramer multiple comparisons adjustment procedure in case of statistical significance. ETHICS AND DISSEMINATION: The Ethics Committee of the University Hospital of Padova, coordinating centre of the trial, deemed the project to be a negligible risk study and approved it through an expedited review process. The results of the study will be disseminated in peer-reviewed journals, and at national and international scientific conferences. Based on the study results, the PediAppRREST app will be further refined and will be available for download by institutions/healthcare professionals. TRIAL REGISTRATION NUMBER: NCT04619498; Pre-results.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".