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Record W3135551229 · doi:10.1186/s12873-021-00416-4

Multi-centre implementation of an Educational program to improve the Cardiac Arrest diagnostic accuracy of ambulance Telecommunicators and survival outcomes for sudden cardiac arrest victims: the EduCATe study design and methodology

2021· article· en· W3135551229 on OpenAlexaffabout
Christian Vaillancourt, Manya Charette, Sarika Naidoo, Monica Taljaard, Matthew Church, Stephanie Hodges, Shannon Leduc, Jim Christenson, Sheldon Cheskes, Katie N. Dainty, Michael J. Feldman, Judah Goldstein, John M. Tallon, Jennie Helmer, Aaron Sibley, Matthew Spidel, Ian E. Blanchard, Jim Garland, Kathryn Cyr, Jamie Brehaut, Paul Dorian, Colette Lacroix, Sandra Zambon, Venkatesh Thiruganasambandamoorthy

Bibliographic record

VenueBMC Emergency Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsIBM (Canada)University of CalgaryHeart and Stroke FoundationAlberta Health ServicesNova Scotia Health AuthorityNorth York General HospitalSt. Michael's HospitalSunnybrook HospitalProvidence Health CareProvincial Health Services AuthorityOttawa HospitalDalhousie UniversityUniversity of TorontoOttawa Public HealthUniversity of British ColumbiaUniversity of Prince Edward IslandUniversity of Ottawa
Fundersnot available
KeywordsMedicineSudden cardiac arrestMedical emergencySudden cardiac deathPopulationEmergency medicineCardiopulmonary resuscitationBystander effectAutomated external defibrillatorResuscitationInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Sudden cardiac death remains a leading cause of mortality in Canada, resulting in more than 35,000 deaths annually. Most cardiac arrest victims collapse in their own home (85% of the time) and 50% are witnessed by a family member or bystander. Survivors have a quality of life similar to the general population, but the overall survival rate for out-of-hospital cardiac arrest (OHCA) rarely exceeds 8%. Victims are almost four times more likely to survive when receiving bystander CPR, but bystander CPR rates have remained low in Canada over the past decade, not exceeding 15-25% until recently. Telecommunication-assisted CPR instructions have been shown to significantly increase bystander CPR rates, but agonal breathing may be misinterpreted as a sign of life by 9-1-1 callers and telecommunicators, and is responsible for as much as 50% of missed OHCA diagnoses. We sought to improve the ability and speed with which ambulance telecommunicators can recognize OHCA over the phone, initiate timely CPR instructions, and improve survival. METHODS: In this multi-center national study, we will implement and evaluate an educational program developed for ambulance telecommunicators using a multiple baseline interrupted time-series design. We will compare outcomes 12 months before and after the implementation of a 20-min theory-based educational video addressing barriers to recognition of OHCA while in the presence of agonal breathing. Participating Canadian sites demonstrated prior ability to collect standardized data on OHCA. Data will be collected from eligible 9-1-1 recordings, paramedic documentation and hospital medical records. Eligible cases will include suspected or confirmed OHCA of presumed cardiac origin in patients of any age with attempted resuscitation. DISCUSSION: The ability of telecommunication-assisted CPR instructions to improve bystander CPR and survival rates for OHCA victims is undeniable. The ability of telecommunicators to recognize OHCA over the phone is unequivocally impeded by relative lack of training on agonal breathing, and reluctance to initiate CPR instructions when in doubt. Our pilot data suggests the potential impact of this project will be to increase absolute OHCA recognition and bystander CPR rates by at least 10%, and absolute out-of-hospital cardiac arrest survival by 5% or more. TRIAL REGISTRATION: Prospectively registered on March 28, 2019 at ClinicalTrials.gov identifier: NCT03894059 .

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)0.005
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 score0.035
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.099
GPT teacher head0.446
Teacher spread0.348 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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