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Record W2527379957 · doi:10.1159/000470636

Comparative Accuracy of Ambulatory ElectrocardiographicAnalysis Systems

2017· article· en· W2527379957 on OpenAlexaff
Terrence J. Montague, Murali Rajaraman, Patricia Montague, Cynthia Spencer, Pentti M. Rautaharju

Bibliographic record

VenueAmerican Journal of Noninvasive Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsUniversity of AlbertaDalhousie University
Fundersnot available
KeywordsMedicineAmbulatoryElectrocardiographyCardiologyAmbulatory ECGInternal medicineElectrodiagnosis

Abstract

fetched live from OpenAlex

Using a beat-to-beat hand count as the true diagnostic standard, the accuracy of 3 Holter systems in the analysis of 15-min electrocardiograms from 20 patients with frequent ventricular ectopic beats (VEBs) and 20 normal control subjects was assessed. The sensitivity of all 3 systems for VEB detection was high (92, 93 and 95%), and there were no differences (NS) in the absolute hand and system counts of total beats or VEBs, nor calculations of mean, maximum and minimum prevailing rates. Percent errors of total beats and mean and maximum prevailing rates were, on average, small (range 1 ± 2 to 3 ± 2%) and similar (NS) among the 3 systems. Percent errors of minimum rate were higher and significantly different (p < 0.01) among systems (10 ± 4 versus 4 ± 3 versus 2 ± 3%). Average VEB errors were even greater (10 ± 14, 8 ± 24 and 4 ± 6%), and there were no intersystem differences (NS). Individual intrasystem VEB errors ranged from 0 to 100%. Thus, commercial Holter systems provide accurate analysis of total beats and most prevailing rate parameters, but a considerably less accurate assessment of VEB frequency. However, enhanced performance may be obtained if specific systems are matched to specific clinical or research objectives.

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)0.001
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 score0.009
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.027
GPT teacher head0.330
Teacher spread0.304 · 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 routes1
Has abstractyes

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