Drug Overdose Leading to Unmasking of Brugada Syndrome
Bibliographic record
Abstract
Brugada syndrome (BrS) is a cardiac autosomal dominant disorder characterized by a constellation of typical electrocardiogram findings as well as clinical criteria including ventricular tachyarrhythmias and sudden death. Although many cases of BrS have been documented, few have identified a drug-induced mechanism as the basis of unmasking the underlying genetic disorder. We present a unique case of a 44-year-old female that was admitted for workup of altered mentation. Upon expanded history, it was evident that multiple psychoactive medications, including amitriptyline, lithium, venlafaxine, and diazepam, taken in duplicity, may have resulted in the observed encephalopathy, and more interestingly, resulted in the unmasking of an underlying BrS with typical electrocardiogram (EKG) changes. Notably, these EKG changes largely reverted to an expected baseline with the cessation of the culprit medications, inferring that drug-induced BrS is a rare but real entity with important implications. This case illustrates that early identification of susceptible individuals creates opportunity for expedited intervention and strategies to screen for familial inheritance to avert lethal arrhythmias and sudden cardiac death. Moreover, as suspected prescription medications become better studied and characterized, informed decision-making can take place to optimize therapeutic management of psychiatric and cardiac comorbidities. J Med Cases. 2018;9(6):179-182 doi: https://doi.org/10.14740/jmc3068w
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".