Secondhand Tobacco Smoke Exposure and Lung Adenocarcinoma <i>In Situ</i> /Minimally Invasive Adenocarcinoma (AIS/MIA)
Bibliographic record
Abstract
The aim of this study was to estimate the effect of exposure to secondhand tobacco smoke on the incidence of lung adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA). Data from seven case-control studies participating in the International Lung Cancer Consortium (ILCCO) were pooled, resulting in 625 cases of AIS/MIA and 7,403 controls, of whom 170 cases and 3,035 controls were never smokers. Unconditional logistic regression was used to estimate adjusted ORs (ORadj) and 95% confidence intervals (CI), controlling for age, sex, race, smoking status (ever/never), and pack-years of smoking. Study center was included in the models as a random-effects intercept term. Ever versus never exposure to secondhand tobacco smoke was positively associated with AIS/MIA incidence in all subjects (ORadj = 1.48; 95% CI, 1.14-1.93) and in never smokers (ORadj = 1.45; 95% CI, 1.00-2.12). There was, however, appreciable heterogeneity of ORadj across studies (P = 0.01), and the pooled estimates were largely influenced by one large study (40% of all cases and 30% of all controls). These findings provide weak evidence for an effect of secondhand tobacco smoke exposure on AIS/MIA incidence. Further studies are needed to assess the impact of secondhand tobacco smoke exposure using the newly recommended classification of subtypes of lung adenocarcinoma.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".