Clinical classification of rare cardiac arrhythmogenic and conduction disorders, and rare arrhythmias
Bibliographic record
Abstract
INTRODUCTION Rare cardiovascular diseases and disorders (RCDDs) constitute an important clinical problem, and their proper classification is crucial for expanding knowledge in the field of RCDDs. OBJECTIVES The aim of this paper is to provide an updated classification of rare arrhythmogenic and conduction disorders, and rare arrhythmias (RACDRAs). METHODS We performed a search for RACDRAs using the Orphanet inventory of rare diseases, which includes diseases with a prevalence of no more than 5 per 10 000 in the general population. We supplemented this with a search of PubMed and Scopus databases according to a wider definition proposed by the European Parliament and the Council of the European Union. RESULTS RACDRAs are categorized into 2 groups, primary electrical disorders of the heart and arrhythmias in specific clinical settings. The first group is further divided into subgroups of major clinical presentation: disorders predisposing to supraventricular tachyarrhythmias, ventricular tachyarrhythmias, bradyarrhythmias, and others. The second group includes iatrogenic arrhythmias or heart rhythm disturbances related to medical treatment, arrhythmias associated with metabolic disorders, and others. We provide a classification of RACDRAs and supplement them with respective RCDDs codes. CONCLUSION The clinical classification of RACDRAs may form a basis to facilitate research and progress in clinical practice, both in diagnostic and therapeutic approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".