P4611Risk for adverse outcome events according to paroxysmal vs. non-paroxysmal atrial fibrillation
Bibliographic record
Abstract
Background: Increasing evidence suggests that sustained forms of atrial fibrillation (AF) are associated with worse outcomes, but long term follow-up data in unselected populations are lacking. In this study, we aimed to assess the risk for adverse events according to AF pattern in a large and unselected cohort of AF patients. Methods: We performed a prospective multicenter observational cohort study of 1540 AF patients. All patients completed questionnaires about personal characteristics and co-morbidities on a yearly basis. AF was classified into paroxysmal and non-paroxysmal AF. All outcomes were centrally validated and included incident hospitalization for congestive heart failure (HF), all-cause mortality and a combined outcome of stroke, myocardial infarction or cardiovascular death (MACE). Multivariable adjusted Cox regression analysis was performed to assess hazard according to AF pattern. Results: Mean age of the population was 69±11 years, paroxysmal AF was observed among 863 (56%) patients and non-paroxysmal among 677 (44%). During a mean follow-up of 3.3±1.4 years, 117, 139 and 150 cases of HF, MACE, and overall deaths occurred, respectively. Compared to patients with paroxysmal AF, patients with non-paroxysmal AF had a higher risk of death or cardiovascular events in age- and sex adjusted models, as shown in the Table. These relationships were substantially attenuated after multivariable adjustment (Table).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".