P5787Atrial fibrillation risk factors and disease severity
Bibliographic record
Abstract
Background: Primary prevention of Atrial fibrillation (AF) is gaining research interest, and could lead to large public health gains. There is a wide variety in AF severity; only some suffer complications such as heart failure (HF), stroke and early death. Primary prevention programs would yield larger gains if they targeted those risk factors associated with AF with complications. Aim: To investigate differences in risk factors for AF with and without complication by HF, stroke, or death before the age of 85 years, in the prospective Malmö Preventive Project (MPP) cohort. Method: Analysis was based on MPP participants without prior AF, HF, or stroke (n=32,586, mean age 45.5 years at baseline, 68.6% men). Subjects were followed until incident AF, via national registers (mean follow-up 27.6 years). AF with complications was defined as incident AF in a subject with concurrent or subsequent HF, stroke or death <85 years' age (n=2,331). This was compared to AF without these complications (n=2,092) using the Lunn-McNeil adaptation of Cox regression for competing risks, in a multivariable model including age, sex, systolic blood pressure, height, body mass index (BMI), current smoking, and prevalent diabetes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.011 | 0.001 |
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".