Associations between tobacco smoking status and patch test results—A cross‐sectional pilot study from the Information Network of Departments of Dermatology (<scp>IVDK</scp>)
Bibliographic record
Abstract
BACKGROUND: Earlier studies suggested a potential association between tobacco smoking and nickel sensitization, but little is known about other contact allergens. OBJECTIVES: To investigate the association of smoking status and contact sensitizations as well as subtypes of dermatitis, and to analyse the sensitization profiles of tobacco smokers. PATIENTS AND METHODS: Within the Information Network of Departments of Dermatology (IVDK), we performed a cross-sectional multicentre pilot study comprising 1091 patch-tested patients from 9 departments, comparing 541 patients with a history of cigarette smoking (281 current and 260 former smokers) with 550 never-smokers. RESULTS: We could not confirm the previously reported association between nickel sensitization and tobacco smoking. Moreover, sensitizations to other allergens, including colophony, fragrance mix I, Myroxylon pereirae and formaldehyde, were not increased in cigarette smokers compared with never smokers. Hand dermatitis (50.6% vs. 33.6%) and occupational cause (36.2% vs. 22.5%) were significantly more frequent among cigarette smokers compared with never-smokers as shown by non-overlapping 95% confidence intervals. CONCLUSIONS: Although our study does not allow a firm conclusion on whether smoking status contributes to certain contact sensitizations, it confirms an association of smoking with hand dermatitis and occupational cause.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".