Estimation and correlation of cigarette smoke exposure in Canadian smokers as determined by filter analysis and biomarkers of exposure
Bibliographic record
Abstract
A clinical study conducted in Canada compared two methods of estimating exposure to cigarette smoke in 192 volunteer subjects: 43 smokers of 4-6 mg, 49 of 8-12 mg and 50 of 14-15 mg ISO tar yield cigarettes and 50 non-smokers. Estimates of mouth level exposure (MLE) to nicotine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), pyrene and acrolein were obtained by chemical analysis of spent cigarette filters. Estimates of smoke constituent uptake were achieved by analysis of urinary biomarkers for total nicotine equivalents (nicotine, cotinine, trans-3'-hydroxycotinine plus their glucuronide conjugates), NNK (total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) plus glucuronide), pyrene (1-hydroxy pyrene plus glucuronide) and acrolein (3-hydroxylpropyl-mercapturic acid) plus the nicotine metabolite cotinine in plasma and saliva. The objective of our study was to confirm the correlations between measures of human exposure obtained by filter analysis and biomarkers. Significant correlations (p<0.001) were found between MLE and the relevant biomarker for each smoke constituent. The adjusted values of the Pearson correlation coefficients (r) were 0.80 (nicotine), 0.77 (acrolein) and 0.44 (pyrene). NNK correlations could not be obtained because of the low NNK yield of Canadian cigarettes. Unexpectedly high levels of acrolein biomarker found in non-smokers urine on one of the two days sampled emphasised the need for more than one sampling occasion per period and an awareness of non-tobacco sources of smoke constituents under investigation. No consistent dose response, in line with ISO tar yield smoked, of MLE estimates was found for nicotine, pyrene and acrolein and respective biomarkers. The influence of demographics on our results has also been examined.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".