Molecular mechanisms and treatment strategies for Dupuytren’s disease
Bibliographic record
Abstract
Molecular mechanisms and treatment strategies for Dupuytren’s disease David B O’Gorman1,2,3,4, Linda Vi1,2,5, Bing Siang Gan1,2,3,5,61Cell and Molecular Biology Laboratory, 2The Hand and Upper Limb Centre, St. Joseph’s Health Care London, Schulich School of Medicine and Dentistry, 3Departments of Surgery, 4Biochemistry, 5Physiology and Pharmacology, 6Medical Biophysics, The University of Western Ontario, London, OT, CanadaAbstract: Dupuytren’s disease (DD) is a common disease of the hand and is characterized by thickening of the palmar fascia and formation of tight collagenous disease cords. At present, the disease is incurable and the molecular pathophysiology of DD is unknown. Surgery remains the most commonly used treatment for DD, but this requires extensive postoperative therapy and is associated with high rates of recurrence. Over the past decades, more indepth exploration of the molecular basis of DD has raised the hopes of developing new treatment modalities. This paper reviews the clinical presentation and molecular pathophysiology of this disease, as well as current and emerging treatment. It also explores the implications of new findings in the laboratory for future treatment.Keywords: Dupuytren’s contracture, Dupuytren’s disease, fibrosis
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".