A Systematic Review and Meta-Analysis on the Strength and Consistency of the Associations between Dupuytren Disease and Diabetes Mellitus, Liver Disease, and Epilepsy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it