Anaesthetic management of patients with Brugada syndrome
Bibliographic record
Abstract
Sir, We read the letter to the editor published in your journal entitled “The baffling issues of Brugada electrocardiogram pattern for anaesthesiologist!” by Rajesh et al. with great interest, and we would like to highlight the omission of a recent multicentric document on this specific topic, not reported in this manuscript that we think could be useful to the scientific community.[12] To date, it is difficult to formulate universal guidelines for anaesthetic management of Brugada syndrome (BrS) patients due to the absence of prospective studies. There is no definitive recommendation for either general or regional anaesthesia, and to the best of our knowledge, there are no large studies ongoing. For this reason, in the anaesthesia management of BrS patients, the decision of using each drug must be made after careful consideration and always in controlled conditions, avoiding other factors that are known to have the potential to induce arrhythmias (or exacerbate the Brugada electrocardiogram pattern) and with a close cooperation between anaesthetists and cardiologists that is essential before and after surgery. We have recently published in The American Journal of Cardiology some general rules,[2] derived from case series and clinical practice, to be followed during the perioperative and anaesthetic management of patients with BrS. The suggestions to be implemented are summarised in this paper and we acknowledge that further prospective investigations are needed. Until strong evidence about this topic is available, we hope to have provided an adequate starting framework with useful suggestions for daily clinical practice.[2] Financial support and sponsorship None. Conflicts of interest There are no conflicts of interest.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".