New electrocardiographic criteria to differentiate the Type-2 Brugada pattern from electrocardiogram of healthy athletes with r'-wave in leads V1/V2
Bibliographic record
Abstract
AIMS: Diagnosis of Type-2 Brugada pattern remains challenging and it could be confused with other electrocardiogram (ECG) patterns presenting an r'-wave in leads V1-V2 like in healthy athletes. This could impact their ability to perform competitive sports. The aim of the study was to evaluate, as a proof of concept, the new ECG criteria to differentiate the Type-2 Brugada pattern from the ECG pattern of healthy athletes depicting an r'-wave in leads V1-V2. METHODS AND RESULTS: Surface ECGs from 50 patients with Brugada syndrome and type-2 Brugada pattern and 58 healthy athletes with an r'-wave in leads V1-V2 were analysed. Different criteria based on the characteristics of the triangle formed by the ascendant and descendant arms of the r'-wave in leads V1-V2 were compared. The duration of the base of the triangle at 0.5 mV (5 mm) from high take-off ≥160 ms (4 mm) has a specificity (SP) of 95.6%, sensitivity (SE) 85%, positive predictive value (PPV) 94.4%, and negative predictive value (NPV) 87.9%. The duration of the base of the triangle at the isoelectric line ≥60 ms (1.5 mm) in leads V1-V2 has an SP of 78%, SE 94.8%, PPV 79.3%, and NPV 93.5%. The ratio of the base at isoelectric line/height from the baseline to peak of r'-wave in leads V1-V2 has an SP of 92.1%, SE 82%, PPV 90.1%, and NPV 83.3%. CONCLUSIONS: The three new ECG criteria were accurate to distinguish the Type-2 Brugada pattern from the ECG pattern with an r'-wave in healthy athletes. The duration of the base of the triangle at 0.5 mV from the high take-off is the easiest to measure and may be used in clinical practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".