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Effectiveness of a Novel Tablet Application in Reducing Guideline Deviations During Pediatric Cardiac Arrest

2023· article· en· W4385514638 on OpenAlexaff
Francesco Corazza, Marta Arpone, Giacomo Tardini, Valentina Stritoni, Giulia Mormando, Alessandro Di Graziano, Paolo Navalesi, Elena Fiorese, Sofia Portalone, Marco De Luca, Marco Binotti, Luca Tortorolo, Serena Salvadei, Alessia Nucci, Alice Monzani, Giulia Genoni, Marco Bazo, Adam Cheng, Anna Chiara Frigo, Liviana Da Dalt, Silvia Bressan

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersUniversità degli Studi di Padova
KeywordsMedicineRandomized controlled trialChecklistGuidelineCardiopulmonary resuscitationAnesthesiologyEmergency medicineWorkloadPhysical therapyMedical emergencyPediatricsResuscitationSurgeryPsychologyAnesthesia

Abstract

fetched live from OpenAlex

Importance: Deviations from international resuscitation guidelines during the management of pediatric cardiac arrest are frequent and affect clinical outcomes. An interactive tablet application (app), PediAppRREST, was developed to reduce guideline deviations during pediatric cardiac arrest. Objective: To assess the effectiveness of PediAppRREST in improving the management of simulated in-hospital pediatric cardiac arrest. Design, Setting, and Participants: This multicenter 3-group simulation-based randomized clinical trial was conducted from September 2020 to December 2021 at 4 Italian university hospitals (Padua, Florence, Rome, Novara). Participants included residents in pediatrics, emergency medicine, and anesthesiology. Analyses were conducted as intention-to-treat. Data were analyzed from January to June 2022. Interventions: Teams were randomized to 1 of 3 study groups: an intervention group that used the PediAppRREST app; a control group that used a paper-based cognitive aid, the Pediatric Advanced Life Support (PALS) pocket card; and a control group that used no cognitive aids. All the teams managed the same standardized simulated scenario of nonshockable pediatric cardiac arrest. Main Outcomes and Measures: The primary outcome was the number of deviations from guidelines, measured by a 15-item checklist based on guideline recommendations. The main secondary outcomes were quality of chest compressions, team clinical performance (measured by the Clinical Performance Tool), and perceived team leader's workload. Study outcomes were assessed via video reviews of the scenarios. Results: Overall 100 teams of 300 participants (mean [SD] age, 29.0 [2.2] years; 195 [65%] female) were analyzed by intention-to-treat, including 32 teams randomized to the PediAppRREST group, 35 teams randomized to the PALS control group, and 33 teams randomized to the null control group. Participant characteristics (210 pediatric residents [70%]; 48 anesthesiology residents [16%]; 42 emergency medicine residents [14%]) were not statistically different among the study groups. The number of deviations from guidelines was significantly lower in the PediAppRREST group than in the control groups (mean difference vs PALS control, -3.0; 95% CI, -4.0 to -1.9; P < .001; mean difference vs null control, -2.6; 95% CI, -3.6 to -1.5; P < .001). Clinical Performance Tool scores were significantly higher in the PediAppRREST group than control groups (mean difference vs PALS control, 1.4; 95% CI, 0.4 to 2.3; P = .002; mean difference vs null control, 1.1; 95% CI, 0.2 to 2.1; P = .01). The other secondary outcomes did not significantly differ among the study groups. Conclusions and Relevance: In this randomized clinical trial, the use of the PediAppRREST app resulted in fewer deviations from guidelines and a better team clinical performance during the management of pediatric cardiac arrest. Trial Registration: ClinicalTrials.gov Identifier: NCT04619498.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.310
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

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