Association between objective measures and parent-reportedmeasures of child tobacco smoke exposure: A secondary dataanalysis of four trials
Bibliographic record
Abstract
INTRODUCTION: Tobacco smoke exposure (TSE) harms children and adults. Studies of childhood TSE exposure often relies on parental reports, but may benefit from objective measures. The objective of our study was to study the relationship between reported and objective measures of TSE. METHODS: We analyzed data from four intervention trials, conducted in clinical or community settings, to identify objective measures most closely associated with parent-reported measures and the optimal set of parent-reported measures for predicting objective measures. We also assessed whether there was a learning curve in reported exposure over time, and the importance of replicate biomarker measures. RESULTS: Correlations between objective and parent-reported measures of child TSE were modest at best, ranging from zero to 0.41. Serum cotinine and urinary cotinine were most strongly associated with parental reports. Parental questions most closely related to biomarkers were number of cigarettes and home smoking rules; together these formed the best set of predictive questions. No trial included all objective measures and all questions, precluding definitive statements about relative advantages. Within-subject repeatability of biomarker measures varied across studies, suggesting that direct pilot data are needed to assess the benefit of replicate measurements. CONCLUSIONS: Improvements in objective and parent-reported child exposure measurements are needed to accurately monitor child TSE, evaluate efforts to reduce such exposure, and better protect child health.
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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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".