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Record W3206592054 · doi:10.1017/cjn.2021.241

Detection of Atrial Fibrillation in Routine EEG Recordings

2021· review· en· W3206592054 on OpenAlexaffvenue
Mohamed Shelig, M.J. Ames, G. Bryan Young

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2021
Typereview
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsWestern UniversityWilfrid Laurier University
Fundersnot available
KeywordsAtrial fibrillationElectroencephalographyMedicineCardiologyInternal medicineStroke (engine)ElectrocardiographyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: A one-channel electrocardiogram (ECG) channel is recommended during electroencephalogram (EEG) recordings principally to help establish ECG or pulse wave contamination of the ECG EEG. However, the ECG recording, in itself, provides useful clinical information, principally the detection of arrhythmias, especially atrial fibrillation (AF), which indicates heart disease that can predispose to embolic stroke and systemic embolism. We sought to determine the prevalence of AF routine recordings in our EEG laboratory in a general hospital. METHODS: We reviewed the consecutive EEG reports for the past 7 years to determine how often AF was detected in various age groups. RESULTS: We found AF in 0-0.2% per decade of life until age 60-69 years, 2.7% for 70-79 years, 5% for 80-89 years, and 8% for 90-99 years. CONCLUSION: We suggest that the ECG trace should be carefully analyzed for AF, especially in patients over 60 years of age. When detected, it should be brought to the referring doctor's attention.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.066
GPT teacher head0.332
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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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