Robust tobacco smoking self-report in two cohorts: pregnant women or men and women living with or without HIV
Bibliographic record
Abstract
Understanding the true burden of tobacco smoking on adverse pregnancy outcomes is critical in generating appropriate interventions to improve outcomes. Self-reporting of human behaviour that is associated with stigma is associated with underreporting in general and may bias the impact of smoking in studies; however, self-reporting is frequently the most practical method of gleaning this information. The objective of this study was to evaluate concordance between self-reported smoking and concentrations of plasma cotinine, a biomarker of smoking, among participants enrolled in two related HIV cohorts. A total of 100 pregnant women (76 living with HIV [LWH] and 24 negative controls) in their third trimester, and 100 men and non-pregnant women (43 LWH and 57 negative controls) were included. Among all participants, 43 pregnant women (49% LWH and 25% negative controls) and 50 men and non-pregnant women (58% LWH and 44% negative controls) were self-reported smokers. The odds of discordance between self-reported smoking and cotinine levels were not significantly different between self-reported smokers and non-smokers, nor between pregnant women and others, but were significantly increased, regardless of self-reported status, among people LWH compared to negative controls. The overall concordance between plasma cotinine and self-reported data among all participants was 94% with a sensitivity and specificity of 90% and 96%, respectively. Taken together, these data demonstrate that participant surveying in a non-judgemental context can lead to accurate and robust self-report smoking data among both persons LWH and not, including in the context of pregnancy.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".