Bibliographic record
Abstract
This editorial refers to ‘New electrocardiographic criteria to differentiate type 2 Brugada pattern from ECG of healthy athletes with r ′-wave in leads V1/V2’ by Serra et al ., doi:10.1093/europace/euu025. Please let me provide you the answer right away: no, we are not able to predict the diagnosis of Brugada syndrome with 100% certainty … on non-diagnostic electrocardiograms (ECGs). But can we come close? That is the question which Dr Serra together with colleagues from Spain, Canada, Belgium, and Italy asked and wrote about in this issue of the Journal .1 The inheritable arrhythmia syndrome—Brugada syndrome—is characterized on the ECG by a specific coved-type or Type-1 right-precordial J-ST segment and by a propensity for malignant arrhythmias and sudden death. Until recently, this characteristic ECG pattern had to be accompanied by evidence or the suggestion of ventricular arrhythmias and/or familial segregation to make the diagnosis of Brugada syndrome. However, since the latest consensus report2 this prerequisite has been abandoned. With non-diagnostic ECGs in persons in whom the diagnosis is suspected, one may use provocation testing with potent sodium channel blockers (e.g. ajmaline) to confirm or to refute the diagnosis. Treatment is mostly conservative (i.e. avoidance of certain drugs,3 family screening, and long-term follow-up) but may include chronic drug therapy (with quinidine), cardioverter defibrillator implantation, and/or ablation of the arrhythmic substrate. Its prevalence is variable but is about 1 in every 2000 persons4 and its underlying pathophysiological mechanism is disputed but involves depolarization and/or repolarization abnormalities.5 While the Brugada syndrome gained increasing attention since the late 1990s, the number of persons suspected of being affected erupted. It is intuitive that only those persons who have a solid and guideline-approved diagnosis should be regarded as having the Brugada syndrome. However, as we have seen earlier in the long QT syndrome, …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".