Smoking, Binge Drinking, and Drug Use Among Childhood Cancer Survivors: A Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: Childhood cancer survivors are at risk for late effects of therapy, some of which may be exacerbated by smoking, alcohol, or drug use. We undertook a meta-analysis of the literature to determine whether survivors engage in risk-taking behaviors at rates different from their peers/siblings. METHODS: Studies comparing current engagement in risk-taking behaviors between cancer survivors and siblings or matched peers were identified in MEDLINE (1946-), EMBASE (1947-), PsychINFO (1806-), and the Cochrane Controlled Trials Register. Two reviewers assessed publications for inclusion and extracted data independently. Studies were combined using inverse variance weighting to determine odds ratios (OR) and prevalence rates of risk-taking behaviors in survivors compared to controls. RESULTS: Fourteen of 1,713 studies satisfied inclusion criteria. Twelve assessed smoking, six binge drinking, and seven drug use. Among survivors, 22% (95% confidence interval 0.19, 0.26) smoked, 20% (0.08, 0.51) were binge drinkers, and 15% (0.10, 0.23) used drugs. Survivors were less likely than siblings to smoke (OR 0.68 [0.49, 0.96]) or binge drink (OR 0.77 [0.68, 0.88]), but similarly likely to use drugs (OR 0.33 [0.03, 3.28]). Survivors were less likely than matched peers to smoke (OR 0.54 [0.42, 0.70]) or use drugs (OR 0.57 [0.40, 0.82]), but equally likely to binge drink (OR 0.97 [0.38, 2.49]). CONCLUSIONS: Childhood cancer survivors engage in similar or lower rates of risk taking than their siblings/peers. Future studies should identify survivors most likely to benefit from focused interventions, and determine the impact of risk-taking behaviors on the risk for late effects of cancer therapy.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".