Association between cigarette smoking and C-reactive protein in a representative, population-based sample of adolescents
Bibliographic record
Abstract
Although related to inflammatory markers in adults, little is known about the association between cigarette smoking and C-reactive protein (CRP) in adolescent smokers. We examined the association between high-sensitivity CRP (hs-CRP) concentrations and smoking in youth. We used data from a cross-sectional, province-wide survey of a representative sample of youth conducted in Quebec, Canada, in 1999. Data were collected in self-report questionnaires completed by participants and their parents. Participants provided a fasting blood sample, and anthropometric measures were undertaken by trained technicians. The present analysis pertains to 1,501 adolescents aged 13 and 16 years who completed questionnaires and for whom blood samples were available. The independent association between a six-category indicator of smoking status and elevated hs-CRP, defined as a value at least in the 90th percentile of the age- and sex-specific CRP distribution, was assessed in multiple logistic regression analyses controlling for potential confounders. Relative to never-smokers, the odds ratios (95% confidence intervals) for puffers (i.e., never smoked a whole cigarette), those who smoked but not in the past month, light past-month smokers, moderate past-month smokers, and heavy past-month smokers were 1.04 (0.55-1.98), 1.76 (1.06-2.94), 1.39 (0.70-2.76), 2.07 (0.96-4.42), and 2.40 (1.18-4.88), respectively. Our data suggest a positive association between smoking status and elevated CRP in adolescents, and in particular among heavier past-month smokers. Damage related to cigarette smoking may begin soon after tobacco use initiation, reinforcing the preventive message that no level of smoking is safe in youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".