Lack of association of iron metabolism and Dupuytren's disease
Bibliographic record
Abstract
BACKGROUND: Iron accumulation as seen in genetic haemochromatosis is a major cause of hepatic fibrogenesis. A link between chronic liver disease and Dupuytren's disease (DD) is well established, especially in alcoholics. AIM: The aim of the present study was to test the hypothesis that iron accumulation might cause fibrosis of the palmar aponeurosis leading to DD. PATIENTS AND METHODS: We examined iron metabolism, mutations of the HFE gene, serum cholesterol, alcohol consumption, presence of chronic liver disease, diabetes and history of severe manual work in a group of 90 patients who had undergone surgery for a severe form of DD. The tissue removed during surgery was histologically examined to confirm the diagnosis of DD. For a control group, we used 33 healthy subjects with similar profiles. RESULTS: The DD group consisted of 82 men and 8 women. Chronic liver disease was found in 27% of DD patients, compared with 6.1% of control subjects (P = 0.013). A history of hand traumatization was present in 33% of DD patients vs. 15% of control subjects (P = 0.048). Excessive alcohol consumption was present in 35.5% of DD patients compared with 15.1% of controls (P = 0.029). None of the other tested parameters, including the prevalence of HFE gene mutations, showed a significant difference between the two groups. CONCLUSIONS: Iron accumulation does not play a major role in the pathogenesis of DD. However, sex, age, manual labour and alcohol consumption are risk factors for progression of DD. We observed a high incidence of chronic liver disease in patients with DD.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".