Twelve-month outcomes in paroxysmal versus permanent atrial fibrillation: data from the prospective, international GARFIELD registry
Bibliographic record
Abstract
Purpose: Patients with paroxysmal atrial fibrillation (AF) are less likely than those with permanent AF to receive antithrombotic therapy for stroke prevention despite guideline recommendations for thromboprophylaxis irrespective of AF type. We sought to investigate treatment and outcomes in these two groups using data from the GARFIELD Registry. Methods: Consecutive adults (≥18 y) with non-valvular AF diagnosed within the previous 6 weeks and ≥1 additional investigator-defined stroke risk factor(s) were enrolled at 540 sites in 19 countries. Adjusted 1-year outcomes in patients classified as having paroxysmal or permanent AF at study entry were determined using a Cox proportional hazards model. Results: Of the 10,608 patients, 28% had paroxysmal AF and 25% permanent AF. Patients with paroxysmal AF had lower mean±SD risk scores (CHADS2 1.8±1.2 vs 2.2±1.2; CHA2DS2-VASc 3.1±1.6 vs 3.6±1.6; HAS-BLED 1.0±0.8 vs 1.1±0.7; all p<0.001) and were less likely to receive antithrombotic treatment than patients with permanent AF (Table). Events (adjusted) at 1 year did not differ between the two groups. Baseline data and 1-year outcomes *Paroxysmal vs permanent; adjusted for antithrombotics and CHA2DS2-VASc factors. AF, atrial fibrillation; AP, antiplatelet; CI, confidence interval; HR, hazard ratio; VKA, vitamin K antagonist. Conclusion: These observational data suggest that patients with paroxysmal AF are perceived to be at lower risk of adverse events at 1 year, whereas after adjustment they have a similar level of risk to patients with permanent AF.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".