Passive and Active Smoking as Possible Confounders in Studies of Breast Cancer Risk
Bibliographic record
Abstract
To the Editor: Goodman et al. 1 provide a most thoughtful evaluation of the potential impact of traditional risk factors for breast cancer as confounders for a study they are planning on occupational magnetic field exposure and female breast cancer. However, in their MEDLINE search for other potential confounders to evaluate, they somehow missed passive and active smoking as risk factors to be considered. Although there is still uncertainty about whether passive and active smoking are breast cancer risk factors, nine of 11 published epidemiologic studies 2–14 on this relation and three published abstracts 15–17 have positive results. These studies suggest that regular passive smoke exposure among women who have never smoked may increase breast cancer risk by more than 50%. Similarly, among women who have themselves smoked, the risk may be almost doubled, compared with women who have never smoked or been regularly exposed passively to tobacco smoke. This would seem to be the kind of factor that Goodman et al. 1 might want to assess, especially given the high historic prevalence of regular, long-term exposure to passive smoking among women who have never smoked and the high prevalence of active smoking among women. In fact, the alcohol-breast cancer association itself could readily be confounded by tobacco smoke exposure, if women who drink are more likely to smoke or to be around smokers than nondrinkers. It would be prudent for these breast cancer researchers to collect measures that would enumerate lifetime residential and occupational passive smoking measures and an active smoking history, so as not to later risk finding themselves wishing they had considered these potential confounders more carefully. Kenneth C. Johnson
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".