104 House Rules and Clean Kids: The down-low on Tobacco
Bibliographic record
Abstract
Abstract Background Despite multiple published guidelines outlining the potential health risks caused by tobacco smoke, young children continue to be exposed to the detrimental effects of household smoking. Environmental factors also have the potential to influence levels of tobacco exposure in children. Many factors such as comfort can influence the decisions of smoking parents to smoke indoors, increasing potential harm for children. Understanding the correlation between various locations within the household and tobacco exposure is helpful in informing a harm reduction strategy for smokers. This project compared the location of reported tobacco use to detection of the nicotine byproduct cotinine in children’s urine samples. Objectives To determine the impact of smoking location on unintentional tobacco exposure in children. Design/Methods This prospective cross-sectional study focused on children under age ten, since 13% of Canadian children in grades 6 and up have tried a cigarette at least once. Of 286 parents approached during a pediatrician visit, 231 agreed to complete an exposure questionnaire and 132 children were able to provide a urine sample during the visit. A standard ELISA assay was used to measure urine cotinine. Results About half of the 31% of households that reported smoking had an indoor smoking ban. Some indoor smokers isolated their activity to the garage (56%). Of the 84 children with detectable urine cotinine, 62 lived in homes that reported smoking. This suggests that some children were exposed to tobacco smoke through other sources or the underestimation of potential tobacco exposure. Fifteen percent of children from smoking homes had cotinine levels similar to nonsmoking homes. Children of indoor smokers were more likely to have detectable cotinine than those of outdoor smokers. Conclusion Roughly 50% of smokers with children have an indoor smoking ban as a harm reduction strategy. In our study, children of smokers with an indoor smoking ban were less likely to have detectable urine cotinine. Although not smoking is the best strategy, limiting smoking to outside is an optimal harm mitigation strategy. For families with indoor smokers, encouraging them to isolate smoking to a single space like the garage may decrease unintentional pediatric exposure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".