The accuracy of self-reported smoking: A systematic review of the relationship between self-reported and cotinine-assessed smoking status
Bibliographic record
Abstract
INTRODUCTION: Smoking is a leading cause of premature mortality and preventable morbidity. Surveillance is most often based on self-reported data, but studies have shown that self-reports tend to underestimate smoking status. METHODS: This study systematically reviewed the literature to measure the concordance between self-reported smoking status and smoking status determined through measures of cotinine in biological fluids. Four electronic databases were searched to identify observational and experimental studies on adult populations over the age of 18 years. RESULTS: Searching identified 67 studies that met the eligibility criteria and examined the relationship between self-reported smoking and smoking confirmed by cotinine measurement. Overall, the data show trends of underestimation when smoking prevalence is based on self-report and varying sensitivity levels for self-reported estimates depending on the population studied and the medium in which the biological sample is measured. Sensitivity values were consistently higher when cotinine was measured in saliva instead of urine or blood. Meta-analysis was not appropriate because of the substantial heterogeneity among the cutpoints used to define smokers and the poor reporting on outcomes of interest. DISCUSSION: Further research in this field would benefit from the standardization of cutpoints to define current smokers and the implementation of standard reporting guidelines to enhance comparability across studies. Accurate estimation of smoking status is important as data from population studies such as those included in this review are used to generate regional and national estimates of smoking status and in turn are used to allocate resources and set health priorities.
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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 | 0.026 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".