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Record W4391542715 · doi:10.12968/jpar.2024.16.2.52

Use of cognitive aids in paediatric out-of-hospital cardiac arrest

2024· article· en· W4391542715 on OpenAlexaff
SunHee Chung, Grace Walker-Stevenson, Carl Eriksson, Matthew Hansen, Garth Meckler, Jeanne‐Marie Guise

Bibliographic record

VenueJournal of Paramedic Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineCardiopulmonary resuscitationEmergency medical servicesMedical emergencyResuscitationEmergency medicine

Abstract

fetched live from OpenAlex

Background: Paediatric cardiac arrest resuscitation is a high-stakes, rare event that can cause high stress levels in emergency medical services (EMS) practitioners. The demands of this work could raise cognitive load on practitioners, which may contribute to adverse safety events. Methods: An EMS agency survey was developed as part of a larger study into organisation-level factors that affect paediatric out-of-hospital cardiac arrest care. Questions focused on the types and numbers of cognitive aids, and whether a paediatric emergency care coordinator (PECC) was present. The number and frequency of these aids were analysed, and statistical significance assessed. The number and type of aids were stratified according to the presence of a PECC and paediatric call volumes. Results: The number of available resources ranged from 0 to 4, with a mean of 2.6 and a median of 3; the average number used was 2.0. These figures are higher than for adults. The most commonly available resources were local protocols, followed by local medication/equipment guides and Broselow tape. The least commonly available were paediatric advanced life support cards. No significant differences were found between the number of resources and the presence of a PECC or call volume. Conclusion: There is wide variability of resources to support EMS providers in the resuscitation of infants and children in out-of-hospital cardiac arrest.

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)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.335
Teacher spread0.312 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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