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Record W1996678991 · doi:10.1002/ijc.22611

Reply to the letter to the editor Lissowska J, Brinton LA, Zatonski W, Blair A, Bardin‐Mikolajczak A, Peplonska B, Sherman ME, Szeszenia‐Dabrowska N, Chanock S, García‐Closas M. Tobacco smoking, NAT2 acetylation genotype and breast cancer risk. Lissowska et al. (Int J Cancer 2006; 119:1961–69). More evidence for passive and active smoking and breast cancer risk among younger women

2007· letter· en· W1996678991 on OpenAlexaffabout
Kenneth C. Johnson

Bibliographic record

VenueInternational Journal of Cancer · 2007
Typeletter
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsMedicinePassive smokingBreast cancerConfidence intervalDemographyCohort studyCohortGynecologyCancerTobacco smokeInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Dear Sir, Lissowska et al. are to be congratulated on collecting lifetime residential and workplace exposure to secondhand smoke for their analysis of passive and active smoking and breast cancer in Poland.1 The report adds to the mounting evidence that active smoking is associated with increased breast cancer risk in younger/premenopausal women,2 (Table 6 in that review) as well as a more general trend toward studies observing increased breast cancer risk associated with smoking including 3 large American cohort studies,3, 4, 5 a large Canadian cohort study,6 two European cohorts7, 8 and studies that have controlled for passive smoking.9 Although the authors conclude that the “data indicate that passive smoking is not associated with breast cancer risk”, they present passive smoking breast cancer risk estimates of 1.28 (95% confidence interval (CI) 0.52–3.11) and 1.27 (95% CI 0.76–2.11) for women aged less than 45 and 45–55, respectively. The estimates are not statistically significant, however they are consistent with results for younger women from three recently published meta-analyses, which found elevated passive smoking summary risk estimates: 2 based on 14 studies of younger, primarily premenopausal women (1.68 (95% CI 1.33–2.12)),9, 10 (Figure 1) and most recently the estimate in the US Surgeon General's 2006 report based on 11 studies of premenopausal women (1.64 (95%CI 1.25–2.14)) (Table 7–10)10 Furthermore, Lissowska et al.'s passive smoking results among older women were also consistent with these meta-analyses—both finding little indication of increased risk for all exposed postmenopausal women.9, 10, 10 Finally, the negative conclusion of the authors is based on very low power in the critical younger age groups. Summary of 14 studies of breast cancer risk associated with passive smoking in younger/premenopausal women 9,10 and Lissowska et al. 1 for age <45 and age 45–55. The passive smoking dose-response analysis presented in the report is inadequate because it only examined risks among all-aged women combined. For women over age 55 the passive risk point estimate was only 1.04, 60% of the passively exposed women were in this age group and these older women likely dominated the highest cumulative exposure category given they have had the longest time period to accumulate exposure. The authors have provided dose-response analyses by age group for active smoking—the same is warranted for passive smoking given the difference in risk estimates by age group. Even though the power would be low, it would be informative for the authors to present tables of breast cancer risk for tertiles of hours/day-years of passive exposure for never active smokers for each of the following categorizations: (i) women under 45; (ii) women 45–55 years; (iii) women under 45 and 45–55 combined given the passive risk estimates were similar for under <45 and 45–55, and to help stabilize risk estimates; (iv) premenopausal women, (the report states that the age groupings were chosen to approximate pre, peri and post menopausal status—why not use menopausal status itself as well, given that it was collected); and (v) for women under 50, as the risk from the passive and active analysis suggests the risks are concentrated in younger women as do meta-analyses. Analyses which collapse the least exposed tertile of passively-exposed women with the never exposed would also help to stabilize risks, but at the expense of some misclassification. Presentation of these analyses would provide a more complete picture of this important work. Yours sincerely, Kenneth C Johnson Ph.D.*, * Evidence and Risk Assessment Division, Centre for Chronic Disease Prevention and Control Public Health Agency of Canada, Ottawa, ON, Canada.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.323
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations2
Published2007
Admission routes2
Has abstractyes

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