Abstract 246: Validating the Accuracy of Routine Electrocardiogram Arrhythmia Diagnosis From an Electronic Repository
Bibliographic record
Abstract
Background: Most health services research uses claims or administrative data for rhythm diagnosis. Including coded data from electrocardiogram (ECG) repositories may enhance diagnostic yield, but validation is needed. Objective: We performed a diagnostic study comparing the Marquette Universal System for Electrocardiography (MUSE, GE Healthcare) coded ECG interpretation of atrial fibrillation and atrial flutter (AF/AFL) and conduction diseases with that of expert over-readers. Methods: We used the institutional MUSE repository to develop ECG cohorts for AF/AFL (n = 369) and conduction diseases (n = 800). We randomly selected 50 cardiologist-interpreted ECGs coded for each diagnosis, plus competing diagnoses and normal controls. Two reviewers unaware of MUSE interpretation independently interpreted all ECGs, with discrepancies resolved by consensus (Reference). We tested agreement between MUSE and Reference using Cohen’s kappa statistic, and assessed diagnostic accuracy with sensitivity and specificity. Results: For both AF/AFL and conduction diseases, agreement between MUSE and Reference was acceptable (See Table 1). Sensitivity of MUSE for AF/AFL was between 60.6 - 75.7% depending on diagnostic criteria. Sensitivity for conduction diseases ranged from 68.1% for non-specific interventricular conduction block to > 88% for 2nd or 3rd degree atrioventricular block. Specificity was ≥ 93% for all diagnoses. Conclusion: Routine ECG interpretation using MUSE coding is highly specific and moderately sensitive. These findings validate use of MUSE data to enhance AF/AFL and conduction diseases case identification algorithms based on claims or administrative data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".