An examination of acrylamide and cadmium as possible mediators of the association between cigarette smoking and chronic musculoskeletal pain
Bibliographic record
Abstract
ABSTRACT: Chronic musculoskeletal pain (CMP) causes significant health loss worldwide and is one of the major public health issues of our time. Cigarette smoking is an independent risk factor of CMP. The present study examined the potential mediating role of 2 subproducts of cigarette smoke, acrylamide and cadmium, individually and combined, on the association between cigarette smoking and CMP, using the Inverse Odds Ratio Weighting (IORW) method. Analyses were conducted on data from 3670 adults who participated to National Health and Nutrition Examination Surveys 2003 to 2004. When smoking was measured with serum cotinine levels, there was an association of moderate and heavy smoking {adjusted Odds Ratio [aOR] >30 ng/mL = 1.99 (95% confidence interval [CI]: 1.44-2.74)} with CMP, but no association between light smoking and CMP (aOR 1-30 ng/mL = 1.17 [95% CI: 0.75-1.80]) as compared to nonsmoking. Small indirect effects were identified through acrylamide (aOR = 1.24 [95% CI: 0.96-1.61]) and cadmium (aOR = 1.56 [95% CI: 0.92-2.63]) only among moderate and heavy smokers. When both biomarkers were considered together, their indirect effect was larger (aOR = 2.07 [95% CI: 1.32-3.23]). These results suggest that the association between cigarette smoking and CMP is mediated by acrylamide and cadmium and that these substances, also present in food and the environment, may serve as biomarkers of CMP.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".