Smoking causes atrial fibrillation? Further evidence on a debated issue
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common sustained arrhythmia with a prevalence of 1–2% in the general population and 15% in people above 80 years of age.1,2 In 2010, AF was estimated to affect 8.8 m and 6 m adults older than 55 years in Europe and USA, respectively, and its prevalence is expected to double by 2060 leading to an epidemic of AF worldwide. These rates are likely markedly underestimated because AF can be ‘silent’, i.e. totally asymptomatic, and therefore underdiagnosed.3 Patients with AF have a five-fold risk of cardioembolic stroke and a two-fold risk of heart failure.3 Moreover, AF doubles the risk of death independently of other known predictors of mortality and, by causing silent and recurring strokes, favours dementia and disability, worsening quality of life.1 Hence, due to the heavy burden imposed by AF on healthcare systems and communities, the identification of all of the factors that may cause AF is crucial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".