Tracing risk of multiple cardiovascular diseases to smoking-related genes
Bibliographic record
Abstract
This editorial refers to ‘Genetic predisposition to smoking in relation to 14 cardiovascular diseases in UK Biobank’†, by S.C. Larsson et al., on page 3304. Smoking is globally the number one avoidable health threat.1 In Europe, ∼25% of all cardiovascular deaths are secondary to smoking (and ∼40% of all fatal cancer cases).1 The prevalence of daily smoking is as low as 9% in people living in Uzbekistan and as high as 38% in Montenegro, with most countries ranging between 20% and 30%.1 In Eastern European countries, smoking is predominantly affecting men, whereas in Western Europe there is little difference between genders. While these numbers are alarming, there is also some good news. The prevalence of smoking in Europe is declining at a rate of ∼1.5% per year and, in particular, the numbers of children smoking at the ages of between 11 and 15 years has decreased substantially from 32% to 16% over the last 15 years.1 These positive trends reflect broad acceptance of the harmful effects of smoking and subsequent political measures to discourage smoking. However, has enough been done to become tobacco-free societies within 20 years as has been announced by Ireland, the UK (Scotland), Finland, and The Netherlands?1 And why doesn’t every European country share this vision?
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".