Long-term risk of adverse outcomes according to atrial fibrillation type
Bibliographic record
Abstract
Sustained forms of atrial fibrillation (AF) may be associated with a higher risk of adverse outcomes, but few if any long-term studies took into account changes of AF type and co-morbidities over time. We prospectively followed 3843 AF patients and collected information on AF type and co-morbidities during yearly follow-ups. The primary outcome was a composite of stroke or systemic embolism (SE). Secondary outcomes included myocardial infarction, hospitalization for congestive heart failure (CHF), bleeding and all-cause mortality. Multivariable adjusted Cox proportional hazards models with time-varying covariates were used to compare hazard ratios (HR) according to AF type. At baseline 1895 (49%), 1046 (27%) and 902 (24%) patients had paroxysmal, persistent and permanent AF and 3234 (84%) were anticoagulated. After a median (IQR) follow-up of 3.0 (1.9; 4.2) years, the incidence of stroke/SE was 1.0 per 100 patient-years. The incidence of myocardial infarction, CHF, bleeding and all-cause mortality was 0.7, 3.0, 2.9 and 2.7 per 100 patient-years, respectively. The multivariable adjusted (a) HRs (95% confidence interval) for stroke/SE were 1.13 (0.69; 1.85) and 1.27 (0.83; 1.95) for time-updated persistent and permanent AF, respectively. The corresponding aHRs were 1.23 (0.89, 1.69) and 1.45 (1.12; 1.87) for all-cause mortality, 1.34 (1.00; 1.80) and 1.30 (1.01; 1.67) for CHF, 0.91 (0.48; 1.72) and 0.95 (0.56; 1.59) for myocardial infarction, and 0.89 (0.70; 1.14) and 1.00 (0.81; 1.24) for bleeding. In this large prospective cohort of AF patients, time-updated AF type was not associated with incident stroke/SE.
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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 | 0.001 |
| 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) | 0.001 | 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".