Physician Preferences in Using Novel Digital Devices for the Management of Atrial Fibrillation—A DAS‐CAM III Survey
Bibliographic record
Abstract
AIM: A recent European Heart Rhythm Association (EHRA) practical guide provides guidance on the use of novel digital devices for heart rhythm analysis using either electrocardiogram (ECG) or photoplethysmography (PPG) technology for the diagnosis of atrial fibrillation (AF). This survey assesses physicians' preferences to use digital devices in patients with possible AF and their impact on clinical decision-making. METHODS AND RESULTS: Participants of the DAS-CAM III initiated and distributed an online survey assessing physician preferences in using digital devices for the management of AF in different clinical scenarios. A total of 505 physicians (median age: 38 [IQR 33-46] years) from 30 countries completed the survey. A third of respondents were electrophysiologists, the others were cardiologists, cardiology residents, or general practitioners. Electrophysiologists were more likely to have experience with both ECG-based (92% vs. 68%, p < 0.001) and PPG-based (60% vs. 34%, p < 0.001) digital devices. The initial diagnostic approach to each scenario (symptomatic low-risk, symptomatic high-risk, or asymptomatic high-risk patient) was heterogeneous. Electrophysiologists preferred intermittent single-lead ECG monitoring to traditional Holter ECGs to screen for AF. Both electrophysiologists and non-electrophysiologists would rarely use PPG-based devices to diagnose and screen for AF (8.2%-9.8%). Electrophysiologists and non-electrophysiologists use ECG-based technology to confirm PPG-documented tracings suggestive of AF. CONCLUSION: While PPG-based digital devices are rarely used for diagnosis and screening for AF, intermittent ECG-based digital devices are beginning to be implemented in clinical practice. More education on the potential of novel digital devices is required to achieve diagnostic pathways as suggested by the EHRA practical guide.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".