A virtual platform to deliver ambulatory care for patients with atrial fibrillation
Bibliographic record
Abstract
Background There are little data on the use of virtual care for patients with arrhythmia. We evaluated a virtual clinic platform, in conjunction with specialist care, for patients with symptomatic atrial fibrillation (AF). Methods This was a prospective, observational cohort study evaluating an online educational and treatment platform, with a randomized sub-study examining the use of an ambulatory single-lead electrocardiogram heart monitor (AHM). Follow-up was 6 months. The main outcome was patients' platform use; success was defined as 90% of patients using the platform at least once, and 75% using it at least twice. The primary outcome in the AHM sub-study was Atrial Fibrillation Symptom Severity (AFSS) score. Other outcomes included patient satisfaction questionnaires, quality of life, emergency department visits, and hospitalizations for AF. Results We enrolled 94 patients between July 2018 and May 2019; 83% of patients logged in at least once and 54.3% more than once. Patients who were older, were male, or had new-onset AF were more likely to log in to the platform. Satisfaction scores were high; 70%–94% of patients responded favorably. Quality-of-life scores improved at 3 and 6 months. In the AHM sub-study (n = 71), those who received an AHM had lower AFSS scores (least square mean difference -2.52, 95% CI -4.48 to -0.25, P = .03). There was no difference in emergency department visits or hospitalizations. Conclusion The online platform did not reach our feasibility target but was well received. Allocation of an AHM was associated with improved quality of life. Virtual AF care shows promise and should be evaluated in further research.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".