Vitamin C is not the Missing Link Between Cigarette Smoking and Spinal Pain
Bibliographic record
Abstract
STUDY DESIGN: A nationwide cross-sectional study. OBJECTIVES: To measure the associations between cigarette smoking (defined as serum cotinine concentration >15 ng/mL) and the 3-month prevalence of spinal pain (neck pain, low back pain, low back pain with pain below knee, and self-reported diagnosis of arthritis/rheumatism) and related limitations, and to verify whether these associations are mediated by serum concentrations of vitamin C. SUMMARY OF BACKGROUND DATA: Cigarette smoking has been consistently associated with back pain, but this association has never been explained. Because vitamin C has recently been reported to be associated with spinal pain and related functional limitations, and the metabolism of vitamin C differs between smokers and nonsmokers, we hypothesized that the prevalence of spinal pain and related limitations might be greater among smokers because they are more susceptible to be in a state of hypovitaminosis C. METHODS: We conducted secondary analyses of National Health and Nutrition Examination Survey (NHANES) 2003 to 2004 data on 4438 individuals aged ≥20 years. RESULTS: Serum concentrations of vitamin C and cotinine were strongly and inversely correlated (r = -0.35, P < 0.0001). Smoking was statistically associated with the prevalence of neck pain [adjusted odds ratio: aOR: 1.25; 95% confidence interval (95% CI): 1.06-1.47], low back pain (aOR: 1.20; 95% CI: 1.04-1.39), and low back pain with pain below knee (aOR: 1.58; 95% CI: 1.13-2.22) and related limitations, with a dose-response relationship (P < 0.05). However, the associations between smoking and spinal pain were not mediated by concentrations of vitamin C. CONCLUSION: These results confirm the relationship between smoking and spinal pain, but they do not support a mediating effect of vitamin C on this relationship. LEVEL OF EVIDENCE: 2.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".