Use, misuse, and pitfalls of the drug challenge test in the diagnosis of the Brugada syndrome
Bibliographic record
Abstract
The diagnosis of Brugada syndrome (BrS) requires the presence of a coved (Type 1) ST segment elevation in the right precordial leads of the electrocardiogram (ECG). The dynamic nature of the ECG is well known, and in patients with suspected BrS but non-diagnostic ECG at baseline, a sodium channel blocker test (SCBT) is routinely used to unmask BrS. There is little doubt, however, that in asymptomatic patients, a drug-induced Brugada pattern is associated with a much better prognosis compared to a spontaneous Type 1 ECG. The SCBT is also increasingly used to delineate the arrhythmogenic substrate during ablation studies. In the absence of a "gold standard" for the diagnosis of BrS, sensitivity and specificity of the SCBT remain elusive. By studying patient groups with different underlying diseases, it has become clear that the specificity of the test may not be optimal. This review aims to discuss the pitfalls of the SCBT and provides some directions in whom and when to perform the test. It is concluded that because of the debated specificity and the overall very low risk for future events in asymptomatic individuals, patients should be properly selected and counseled before SCBT is performed and that SCBT should not be performed in asymptomatic patients with a Type 2 Brugada pattern and no family history of BrS or sudden death.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".