Longitudinal smoking patterns in survivors of childhood cancer: A Childhood Cancer Survivor Study (CCSS) update.
Bibliographic record
Abstract
10075 Background: Survivors of pediatric cancer have elevated risks of mortality and morbidity. Many morbidities associated with cancer treatment (e.g. second cancers, cardiac and pulmonary disease) are associated with cigarette smoking, suggesting survivors who smoke may be at higher risk for these adverse health conditions. Methods: We examined self-reported smoking status in 10,430 CCSS participants (age ≥ 18 years) across 2 questionnaires, at a median time of 7.9 years (range 1.4-11.9) apart. Smoking prevalence was compared among survivors, siblings, and the U.S. general population (standardized by age, sex, race/ethnicity and calendar time). Among a subgroup of survivors who also completed an additional follow-up questionnaire (N = 3908) a median of 12.5 years (range 4.3-16.3) after the first questionnaire, multivariable regression models evaluated characteristics associated with longitudinal smoking patterns. Results: At baseline, 19% of survivors were current smokers, compared with 24% of siblings and 29% in the standardized U.S. general population. At first follow-up, 17% of survivors were current smokers, compared to 21% of siblings and 24% of the U.S. population. Characteristics associated with consistent “never smoking” over all three questionnaires included higher household income (RR 1.17, 95% CI 1.08-1.25 for ≥ $60,000 versus < $20,000 per year), higher education (RR 1.36, 95% CI 1.26-1.47 for > high school versus ≤ high school), and receipt of cranial radiation therapy (RR 1.10, 95% CI 1.05-1.16). Among “ever smokers”, higher income (RR 1.22, 95% CI 1.09-1.38) and education (RR 1.26, 95% CI 1.13-1.40) were associated with quitting, whereas cranial radiation was associated with not having quit (RR 0.85, 95% CI 0.76-0.96). Development of an adverse health condition was not associated with smoking patterns. Conclusions: Although smoking prevalence may be declining, the substantial number of consistent, current smokers reinforces the need for continued development of effective smoking interventions for survivors.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".