Association between Smoking and Dupuytren Contracture: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Smoking is a commonly cited risk factor for Dupuytren disease; however, evidence on both the directionality and strength of its association has remained inconclusive. The current study aimed to quantify the association between smoking and the prevalence of Dupuytren disease. METHODS: A systematic database search was conducted to identify comparative studies reporting Dupuytren disease prevalence in smokers and nonsmokers (CRD420251043113). Results were pooled using pairwise meta-analysis with a random-effects model. RESULTS: Twenty-two studies were included in the analysis, encompassing 609,195 smokers and 906,297 nonsmokers. The prevalence of Dupuytren disease was 5.6% among smokers and 4.4% among nonsmokers. Current active smokers were at increased odds of Dupuytren disease compared with nonsmokers (OR, 1.45; 95% CI, 1.06 to 1.98; I ² = 95.8%). To a lesser degree, this risk was also increased among former smokers (OR, 1.39; 95% CI, 1.14 to 1.7; I ² = 97.5%). Overall, any smoking history conferred a 1.5-fold increased odds for Dupuytren disease (OR, 1.50; 95% CI, 1.20 to 1.88; I ² = 95.5%), which remained statistically significant on exclusion of studies with high risk of bias and leave-one-out sensitivity analysis. CONCLUSIONS: This study found a statistically significant association of smoking with Dupuytren disease. Smoking cessation should be encouraged in patients with Dupuytren disease, although further research is needed to identify whether smoking cessation could slow disease progression or prevent recurrence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".