Self-reported cigarette smoking status imprecisely quantifies exposure in pregnancy
Bibliographic record
Abstract
Objective: We sought to determine the validity of self-reported smoking activity versus two quantitative measurements of tobacco exposure in pregnancy. We hypothesized that pregnant women would under-report their daily smoking amounts, due to the negative social stigmas associated with such a behavior. Methods: Cigarette-smoking and non-smoking pregnant women were recruited as part of a larger research study. Pregnant women with a singleton baby (>24 weeks) were recruited at a clinical appointment or prior to an elective caesarian section. Self-reported smoking status, including time since last cigarette, was recorded. End-tidal breath carbon monoxide (ETCO) levels and urine cotinine levels were measured and compared. Results: Both normotensive non-smoking (NTN) (n = 44) and normotensive smoking (NTS) (n = 24) pregnant women were recruited. A strong correlation was found between ETCO levels and urine cotinine measurements (r = 0.6566, p 0.05). Conclusion: Self-reported smoking status accurately identifies women who smoke in pregnancy, but not their level of tobacco exposure. Urine cotinine or ETCO are much better quantitative measurements of nicotine and carbon monoxide, respectively, and should be measured for a more precise indicator of smoking activity. These devices will allow for better counseling and monitoring of women who are trying to quit smoking and/or who enter into smoking cessation programs.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".