The hazards of smoking in women: results from the Million Women Study
Bibliographic record
Abstract
The hazards of smoking in women: results from the Million Women Study Smoking is associated with many diseases and remains a major cause of mortality.The Million Women Study is a large UK prospective cohort study of women born in the second quarter of the 20th century, collecting data for a broad range of health issues.Women were recruited and sent questionnaires that included lifestyle factors such as smoking at baseline, 3 years and 8 years.After excluding women with previous smoking-associated diseases, 1.2 million invited participants were followed up for mortality for a mean of 12 years via national mortality records.Current smokers had a threefold increase in mortality compared with never-smokers, with adjusted mortality rate ratio increasing almost linearly with the number of cigarettes smoked at baseline.Relative risks were highest for deaths due to chronic lung disease and lung cancer.Ex-smokers had lower but still raised relative risks.Applying their results to a hypothetical population, the authors calculated that smokers lose 11 years of life span.The large sample size would minimise any selection bias of the Million Women Study, which recruited volunteers within the UK breast screening programme.With an average starting age of 19 years, this cohort is felt to represent the first generation of women in the UK where the long-term effects of smoking throughout adult life can be observed.The authors provided figures on the proportional reduction in mortality by stopping smoking before certain ages; used in the appropriate context, these data may be a useful tool for smoking cessation advice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".