Cigarette Smoking and Structural Brain Deficits in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Cigarette smoking and atrial fibrillation (AF) are associated with impaired brain health. We investigated the association between smoking habits and brain lesions and volume in patients with AF. In patients with AF from a multicenter cohort study, we assessed smoking status (never, ex-, active), number of cigarettes smoked per day, smoking duration (years), pack-years, and time since smoking cessation. On brain magnetic resonance imaging, the prevalence and volumes of white matter lesions (WML) and small noncortical infarcts, and the volumes of gray matter and white matter were evaluated. Logistic and linear regression analyses were used to analyze the association between smoking habits and brain lesions and volumes. A total of 1,728 patients were enrolled (mean age 72.6 years, 27.5% female); 7.5% were active smokers; 48.5% were ex-smokers, and 44% had never smoked. We found linear associations of number of cigarettes smoked per day, pack-years, and older age at smoking cessation with reduced gray matter volume (p for linear trend <0.01, 0.02, and 0.01, respectively). Patients with a smoking duration in the second and third tertile had a greater risk for WML Fazekas ≥2 (odds ratio 1.86, 95% confidence interval 1.29 to 2.69, p <0.01 and 1.47 [1.02 to 2.12], p=0.04), and exhibited larger WML volumes. Patients who had stopped smoking ≥16 years before enrollment were less likely to have small noncortical infarcts (odds ratio 0.46, 0.25 to 0.88, p=0.02) and had smaller WML volumes (β: −0.451 mm 3 , −0.8 to −0.11, p=0.01). In conclusion, smoking intensity and time since smoking cessation were associated with the presence and volume of brain lesions and with brain volumes in patients with AF.
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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 | 0.002 |
| 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 | 0.001 | 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".