Exposure to tobacco smoke and validation of smoking status during pregnancy in the MIREC study
Bibliographic record
Abstract
Given that prenatal exposure to tobacco smoke can lead to increased risks of adverse health effects, having valid measures of exposure is important. In a Canadian cohort (n = 2000), maternal and infant biospecimens were analysed for cotinine. Sensitivity and specificity of self-reported active smoking status were estimated. Regression modelling was used to identify potential predictors of maternal and infant plasma cotinine in non-smoking women. During the first trimester, 60.6% of the women reported never smoking, 27.3% were former smokers, 6.1% had quit when they found out they were pregnant, 5.8% were smokers and 42% of the non-smokers reported exposure to secondhand smoke (SHS). Low detection of tobacco biomarkers in meconium limited its ability to identify exposure to SHS. The sensitivity and specificity for self-reported smoking during the 1st trimester were 85.37 and 99.45%, respectively. The lowest sensitivity was found in participants with the highest level of education and income, oldest women and those born outside Canada. Non-smoking women living in an apartment had 1.7 times higher odds of detectable plasma cotinine than those living in a single home after adjusting for other variables. Our results suggest that while self-reports are fairly accurate, they may be less so in populations with higher socio-economic status. This investigation underscores the need to consider the participant socio-economic characteristics and dwelling type when using questionnaires to estimate active and passive tobacco 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".