1190Lifestyle risk factors for non-valvular atrial fibrillation
Bibliographic record
Abstract
Background: Lifestyle factors, such as smoking, alcohol intake and obesity are common and associated with cardiovascular diseases. Atrial fibrillation (AF) is the most common arrhythmia and is associated with an increased risk of stroke. There is limited information about the association between lifestyle factors and the risk of AF. Objective: Our study aimed to investigate the association between lifestyle factors and the risk of developing non-valvular AF. Methods: We performed a retrospective nested case-control study comparing patients with a first diagnosis of AF with an age- and gender-matched control group without AF. The study population consisted of all patients aged <85 years in the UK Clinical Practice Research Datalink (CPRD) eligible for linkage to the English inpatient and outpatient Hospital Episodes Statistics (HES). Patients with an incident diagnosis of AF (cases) between 1st April 2008 and 31st March 2014 with at least 365 days of observation in the CPRD-HES link were identified. We excluded patients with a previous recording of intake of parenteral or oral anticoagulants, or antiarrhythmic drugs, or a history of AF or irregular heartbeat, cardioversion, cardiac valve disease or valve surgery. For each AF case, up to 5 controls were matched based on year of birth, gender and the day of the respective case's incident AF (index day). Adjusted odds ratios (OR) of the association between lifestyle factors and AF were derived from a conditional logistic regression model and adjusted for age, gender, socioeconomic status, congestive heart failure or left ventricular dysfunction, diabetes, hypertension, hyperthyroidism, hypothyroidism, pneumonia, renal failure, respiratory failure, sleep apnea, stroke or thromboembolism and vascular disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.016 | 0.002 |
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".