Percutaneous Fasciotomy for the Treatment of Dupuytren's Disease—A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Dupuytren's disease is described as a thickening of the palmar fascia. It typically affects men of Northern European descent in their fifties. The disease process starts as a nodule at the distal palmar crease that progressively gives rise to a cord invading distally toward the finger. Historically, different treatments have been described. Our purpose was to perform a meta-analysis of the evidence published on the percutaneous fasciotomy (PCF) treatment. METHODS: We searched Medline, PubMed, and the Cochrane Library for articles evalu ating the use of PCF for Dupuytren's disease. No study was excluded based on quality. RESULTS: The search yielded nine studies. Because of their different methodologies, a meta-analysis could not be performed. However, we were able to extract common qualitative conclusions. PCF is an effective treatment modality for patients in whom general anesthesia is contraindicated, with a good outcome especially at the metacarpophalangeal joint, a low recurrence rate in the short term, and few complications. CONCLUSIONS: Similar conclusions were reached by all the articles under study. Nevertheless, there remains the need for a prospective study with a higher statistical power and standardized clinical evaluation and surgical methods in order to achieve more objective quantitative results. It would also be pertinent to compare the outcomes and complication rates of PCF with the new collagenase treatment.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".