Passive smoking in relation to lung cancer incidence and histologic types in Norwegian adults: the HUNT study
Bibliographic record
Abstract
Passive smoking has been proposed as a risk factor for lung cancer. The increased risks for lung cancer overall and different histologic types in relation to passive smoking were demonstrated in two meta-analytical studies [1, 2], in which the included studies were mostly case–control designs, which are subject to recall bias. To date, there have been a limited number of prospective studies on passive smoking in relation to lung cancer incidence, most of which included only never-smoking women [3–5]. There is also a lack of longitudinal investigations concerning the influence of passive smoking on different histologic types of lung cancer. We aimed to evaluate the influences of passive smoking during childhood and adulthood on the development of lung cancer and histologic types in a long-term follow-up cohort, and to assess possible sex-related differences in the association. Childhood and adulthood exposure to passive smoking associated with increased risk of lung cancer The Nord-Trøndelag Health Survey (HUNT) is a collaboration between the HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology), the Nord-Trøndelag County Council and the Norwegian Institute of Public Health. Author contributions: YQS, AL, YC and XMM contributed to the study design. XMM and AL contributed to data collection. CW contributed to statistical analyses. YQS conducted statistical analyses, interpreted results and wrote the initial draft of the manuscript. AL, FS, CW, YC and XMM participated in the data interpretation and helped to write the final draft of the manuscript.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".