Expert cardiologists cannot distinguish between Brugada phenocopy and Brugada syndrome electrocardiogram patterns
Bibliographic record
Abstract
AIMS: Brugada phenocopies (BrPs) are electrocardiogram (ECG) patterns that are identical to true Brugada syndrome (BrS) but are induced by various clinical conditions. The concept that both ECG patterns are visually identical has not been formally demonstrated. The aim of our study was to determine if experts on BrS were able to accurately distinguish between the BrS and BrP ECG patterns. METHODS AND RESULTS: Six ECGs from confirmed cases of BrS and six ECGs from previously published cases of BrP were included in the study. Surface 12-lead ECGs were scanned, saved in JPEG format, and sent to 10 international experts on BrS for evaluation (no clinical history provided). Evaluators were asked to label each case as a Brugada ECG pattern or non-Brugada ECG pattern by visual interpretation alone. The overall accuracy was 53 ± 33% for all cases. Within the BrS cases, the mean accuracy was 63 ± 34% and within the BrP cases, the mean accuracy was 43 ± 33%. Intra-observer repeatability was moderate (κ = 0.56) and inter-observer agreement was fair (κ = 0.36) while evaluator accuracy vs. the true diagnosis was only marginally better than chance (κ = 0.05). Similarly, diagnostic operating characteristics were poor (sensitivity 62%, specificity 43%, +LR 1.1, -LR 0.9). CONCLUSION: Our results provide strong evidence that BrP and BrS ECG patterns are visually identical and indistinguishable. These findings support the use of systematic diagnostic criteria for differentiating BrP vs. BrS as an erroneous diagnosis may have a negative impact on patient morbidity and mortality.
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 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 | 0.017 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".