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Record W3177557123 · doi:10.7759/cureus.16260

What Adult Electrocardiogram (ECG) Diagnoses or Findings are Most Important for Advanced Care Paramedics to Know?

2021· article· en· W3177557123 on OpenAlexaffabout
Aaron Sibley, Mathew H MacLeod, Catherine Patocka, Jenny Yu, Henrik Stryhn, Trevor Jain

Bibliographic record

VenueCureus · 2021
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsHolland CollegeUniversity of CalgaryUniversity of Prince Edward Island
Fundersnot available
KeywordsMedicineMedical diagnosisLikert scaleContext (archaeology)Delphi methodMedical emergencyEmergency medical servicesFamily medicinePsychologyPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: The interpretation of electrocardiograms (ECGs) is an essential competency in modern paramedicine. Although educational guidelines for paramedic ECG interpretation exist, they are broad, not evidence-based, and lack prioritization in a prehospital clinical context. We conducted this study to gain consensus among stakeholders (EMS physicians, paramedic educators, and paramedic clinicians) regarding which ECG diagnoses or findings are most important for a practising advanced care paramedic to know. Methods: This study was an internet-based Delphi survey. We purposefully sampled participants in pairs (physician/paramedic) from all 10 Canadian provinces. Individuals rated a previously developed comprehensive list of emergency ECG diagnoses or findings on the importance of paramedic recognition and impact on prehospital care using a 4-point Likert scale. The consensus was achieved with a minimum of 75% agreement on Likert rating for a single diagnosis or finding during survey rounds one to three. When consensus was not reached, stability was defined as a shift of individual ratings between rounds of 20% or less. RESULTS: All 20 participants completed the first and second rounds of the survey, and 17 (85%) completed three rounds. Overall, 32 (26.4%) of 121 potentially important ECG diagnoses or findings reached consensus, 2 (1.7%) reached stability and 87 (71.9%) reached neither consensus nor stability. Twenty-one (17.4%) diagnoses or findings were considered "Very Important", six (4.9%) "Important", and five (4.1%) "Minimally Important". In the first round of the survey, the mean rating of the importance of a paramedic knowing a specific ECG diagnosis or finding was lower in the physician group than the paramedic group on 85 (72%) of 118 initial diagnoses or findings. CONCLUSION: We have created a list of ECG diagnoses or findings prioritized for the prehospital context that may assist paramedic educators in focusing on educational interventions. Many ECG diagnoses or findings failed to reach consensus or stability, demonstrating potential disagreement regarding clinical expectations for ECG knowledge among paramedics or physicians.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.317
Teacher spread0.305 · 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 designNot applicable
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

Citations2
Published2021
Admission routes2
Has abstractyes

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