Evaluation of the accuracy of self-reported smoking in pregnancy when the biomarker level in an active smoker is uncertain
Bibliographic record
Abstract
INTRODUCTION: Our main objective was to estimate smoking prevalence as well as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of self-reported smoking among pregnant women in Edmonton, Canada, at 15-16 weeks of gestation. METHODS: We used serum samples to assemble a cohort of pregnant women who underwent an optional second-trimester screening for chromosomal and developmental anomalies. We determined cotinine concentrations for 92 self-reported smokers (11% of the cohort) and for 285 self-reported nonsmoking mothers, using adapted urinary cotinine assay. Self-reports were collected at the time of delivery. In a validation study, serum cotinine was determined for known smokers and nonsmokers and used, within a Bayesian statistical framework, to define the distribution of cutoffs that differentiate true smokers from nonsmokers. This distribution of cutoffs was used to construct multiple two-by-two tables to obtain the distribution of sensitivity, specificity, PPV, NPV, and prevalence. RESULTS: Sensitivity was poor (M = 47.4%, SD = 17.3%), but specificity was nearly perfect (M = 94.9%, SD = 1.1%). PPV (M = 66.6%, SD = 11.7%) was smaller than NPV (M = 84.7%, SD = 14.3%). In our sample, the prevalence of true smoking at 15-16 weeks of gestation was described by a skewed distribution with a mean of 21.6% (SD = 13.8%) and a median of 16.6%. DISCUSSION: The strength of the present study includes blinding of subjects to the intention to test their sera for a biomarker of smoking. A limitation was the use of a nonrandom sample restricted to pregnancies that resulted in live births. We discuss data collection methods that would elicit more accurate smoking histories from pregnant women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".