Statement from the Asia Summit: Current state of arrhythmia care in Asia
Bibliographic record
Abstract
Abstract On May 27, 2022, the Asia Pacific Heart Rhythm Society and the Heart Rhythm Society convened a meeting of leaders from different professional societies of healthcare providers committed to arrhythmia care from the Asia Pacific region. The overriding goals of the meeting were to discuss clinical and health policy issues that face each country for providing care for patients with electrophysiologic issues, share experiences and best practices, and discuss potential future solutions. Participants were asked to address a series of questions in preparation for the meeting. The format of the meeting was a series of individual country reports presented by the leaders from each of the professional societies followed by open discussion. The recorded presentations from the Asia Summit can be accessed at https://www.heartrhythm365.org/URL/asiasummit‐22 . Three major themes arose from the discussion. First, the major clinical problems faced by different countries vary. Although atrial fibrillation is common throughout the region, the most important issues also include more general issues such as hypertension, rheumatic heart disease, tobacco abuse, and management of potentially life‐threatening problems such as sudden cardiac arrest or profound bradycardia. Second, there is significant variability in the access to advanced arrhythmia care throughout the region because of differences in workforce availability, resources, drug availability, and national health policies. Third, collaboration in the area already occurs between individual countries, but no systematic regional method for working together is present.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".