Arrhythmic Burden and the Risk of Cardiovascular Outcomes in Patients With Paroxysmal Atrial Fibrillation and Cardiac Implanted Electronic Devices
Bibliographic record
Abstract
BACKGROUND: Whether the amount of atrial fibrillation (AF) patients experience conveys important prognostic information beyond that provided by the diagnosis of AF is uncertain. The study objective was to assess the dose-response relationship between device-detected AF burden and subsequent cardiovascular outcomes. METHODS: Among patients with paroxysmal AF who underwent cardiac implantable electronic device implantation (2010-2016), Merlin.net remote-monitoring data were linked to Medicare claims to assess the magnitude and strength of the associations between device-based AF burden (defined as a daily percentage of time spent in AF or maximal AF episode duration ascertained at baseline over 30 days) and key cardiovascular outcomes. RESULTS: <0.001) There was also a dose-response relationship between increasing AF burden and all-cause or cardiovascular hospitalization and ischemic stroke. Updating AF burden data every 30 days did not alter the AF burden-prognostic relationships determined from the use of baseline data alone. Results were also consistent when 3-year outcomes were considered and after accounting for the use of oral anticoagulants. CONCLUSIONS: In paroxysmal AF, there is a clinically relevant dose-response relationship between increasing AF burden and rates of adverse outcomes at 1- and 3-years, including increasing risks of cardiovascular hospitalization, ischemic stroke, and mortality.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".