Tobacco Use Among Siblings of Childhood Cancer Survivors: A Report From the Childhood Cancer Survivor Study
Bibliographic record
Abstract
BACKGROUND: Having a brother or sister with childhood cancer may influence health behaviors during adulthood. The aim of this study was to compare tobacco use in siblings of survivors with peers and to identify factors associated with sibling tobacco use. PROCEDURES: A retrospective cohort study was conducted using adult siblings (N = 1,974) of 5+ year cancer survivors in the Childhood Cancer Survivor Study (CCSS) and participants (N = 24,105, weighted to match CCSS) in the 2007 National Health Interview Survey. Self-reported tobacco use, sociodemographic, and cancer-related risk factors were analyzed. RESULTS: Siblings were equally likely to have ever smoked compared to their peers (odds ratio [OR] 1.02, 95% confidence interval [CI] 0.93-1.12). Siblings were less likely to be current smokers (OR 0.83, 95%CI 0.73-0.94), but more likely to be former smokers (OR 1.21, 95%CI 1.08-1.35). Siblings with low education were more likely to ever smoke (OR 1.51, 95%CI 1.15-2.00) and be current smokers (OR 1.67, 95%CI 1.24-2.26) compared to their peers. Among siblings, risk factors for current tobacco use included the following: low income <$20,000 (OR 1.66, 95%CI 1.09-2.54), low education (OR 6.68, 95%CI 4.07-10.97), psychological distress (OR 5.36, 95%CI 2.21-13.02), and heavy alcohol use (OR 3.68, 95%CI 2.50-5.41). CONCLUSIONS: Siblings of survivors take up smoking at similar rates to their peers, but are more likely to quit. Efforts are needed to address disparities by providing greater psychosocial support and education for the lowest socioeconomic status families facing childhood cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".