A Systematic Review and Meta-Analysis on the Strength and Consistency of the Associations between Dupuytren Disease and Diabetes Mellitus, Liver Disease, and Epilepsy
Bibliographic record
Abstract
BACKGROUND: The role of diabetes mellitus, liver disease, and epilepsy as risk factors for Dupuytren disease remains unclear. In this systematic review and meta-analysis, the strength and consistency of these associations were examined. METHODS: The MEDLINE, EMBASE, and Web of Science databases were searched for articles reporting an association between Dupuytren disease and diabetes mellitus, liver disease, and epilepsy published before September 26, 2016. The frequencies of Dupuytren disease and diabetes mellitus, liver disease, and epilepsy were extracted, as was information on potential confounders. Generalized linear mixed models were applied to estimate pooled odds ratios, adjusted for confounders. Heterogeneity between studies was quantified using an intraclass correlation coefficient and was accounted for by a random effect for study. RESULTS: One thousand two hundred sixty unique studies were identified, of which 32 were used in the meta-analyses. An association between Dupuytren disease and diabetes mellitus was observed (OR, 3.06; 95 percent CI, 2.69 to 3.48, adjusted for age), which was stronger for type 1 diabetes mellitus than for type 2 diabetes mellitus but was not statistically significant (p = 0.24). An association between Dupuytren disease and liver disease was observed (OR, 2.92; 95 percent CI, 2.08 to 4.12, adjusted for sex). Dupuytren disease and epilepsy were associated, yielding an OR of 2.80 (95 percent CI, 2.49 to 3.15). Heterogeneity between studies was moderate to low. CONCLUSIONS: These findings demonstrate an association between Dupuytren disease and diabetes mellitus, liver disease, and epilepsy. Prospective, longitudinal studies are needed to elucidate the pathways causing these associations.
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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 | 0.031 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.048 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".