P3569Impact of body mass index in newly diagnosed atrial fibrillation in the GARFIELD-AF registry
Bibliographic record
Abstract
Purpose: To analyze the association of body mass index (BMI) with comorbidities and outcomes of patients with newly diagnosed atrial fibrillation (AF) and ≥1 stroke risk factor. Methods: 28,628 patients were enrolled from Mar 2010 to Oct 2014 in the prospective GARFIELD-AF registry. BMI data were available for 22,541 patients, stratified as: underweight (3.2%), normal (25.3%), overweight (40.2%), obese (20.1%), and morbidly obese (11.1%). Results: Increasing BMI was associated with younger age and higher rates of hypertension, hypercholesterolemia, type 2 diabetes, coronary artery disease, and CHF. Underweight patients had the highest prevalence of prior stroke/TIA, bleeding, and moderate-to-severe CKD (Table). The proportion of patients with NYHA class III/IV CHF was similar in both morbidly obese and underweight patients. Obese (vs underweight) patients were more likely to receive oral anticoagulants (67.2% vs 53.2%). Crude 2-yr all-cause mortality per 100 person-years (95% CI) was 8.71 (7.20, 10.53) in underweight, 4.50 (4.10, 4.93) normal, 3.13 (2.77, 3.53) obese, and 2.88 (2.35, 3.53) in the morbidly obese (BMI 35-<40 kg/m2). The poorer outcomes in underweight patients persisted after adjustment for baseline factors (figure). Half of deaths in the underweight vs 36.2% in patients with BMI ≥40 kg/m2 were due to non-cardiovascular events.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".