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Record W2763502689 · doi:10.1093/eurheartj/ehx501.p621

P621Electrocardiograms in low-risk patients undergoing an annual health examination

2017· article· en· W2763502689 on OpenAlexaffabout
R. Sacha Bhatia, Zachary Bouck, Noah Ivers, Jeffrey M. Singh, Ciara Pendrith, Graham Mecredy, Dennis T. Ko, Danielle Martin, Harindra C. Wijeysundera, Jack V. Tu, Lynn Wilson, Paul Dorian, Joshua Tepper, Richard H. Glazier, Wendy Levinson

Bibliographic record

VenueEuropean Heart Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesUniversity of CalgaryUniversity of OttawaWomen's College Hospital
Fundersnot available
KeywordsMedicineHealth examinationPhysical examinationEnvironmental healthIntensive care medicineEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

Background/Introduction: Clinical guidelines advise against routine electrocardiograms (ECG) in low-risk, asymptomatic patients, but the frequency and impact of such ECGs are unknown. Purpose: The purpose of this study was to assess the frequency of ECGs following an annual health examination (AHE) with a primary care provider amongst patients with no known cardiac conditions or risk factors, to explore factors predictive of receiving an ECG in this clinical scenario, and to compare downstream cardiac testing and clinical outcomes in low-risk patients who did and did not receive an ECG after their AHE. Methods: Design: We conducted a population-based retrospective cohort study using administrative healthcare databases from Ontario between 2010/11 and 2015/16 to identify low-risk primary care patients and to assess the subsequent outcomes of interest in this time frame. Participants: All patients over the age of 18 who have no prior cardiac history or risk factors who received an AHE. Exposure: Receipt of an ECG within 30 days of an AHE.

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 score0.087
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.262
Teacher spread0.244 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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