Collagenase Clostridium Histolyticum Versus Percutaneous Needle Fasciotomy for Dupuytren’s Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Minimally invasive treatments for Dupuytren’s disease (DD), such as percutaneous needle fasciotomy (PNF) and collagenase clostridium histolyticum (CCH), have become alternatives to open surgeries. This meta-analysis compared these treatments in terms of complications, patient satisfaction, clinical outcomes, and recurrence. Relevant studies up to June 2024 were identified through major databases, following PRISMA guidelines, and the study was registered on PROSPERO. Statistical analysis using Review Manager 5.4 found PNF had lower post-operative rates of oedema (RR = 0.15, 95% CI [0.09, 0.27], p < 0.00001), lymphadenopathy (RR = 0.09, 95% CI [0.02, 0.38], p = 0.0010), and pruritus (RR = 0.1, 95% CI [0.01, 0.73], p = 0.02) compared to CCH. However, there were no significant differences in skin tears, recurrence, reintervention, extension deficit, or residual flexion at metacarpal and proximal interphalangeal joints (p > 0.05). Patient-reported outcomes, including QuickDASH and URAM scores, also showed no significant differences. Eleven studies involving 1443 patients were analysed, and most were at a low-to-moderate risk of bias, as assessed using the Cochrane or Newcastle–Ottawa tools. While PNF showed fewer minor complications, overall clinical and patient-reported outcomes were comparable between the treatments. These findings highlight the need to tailor treatment choices to patient preferences and clinical context.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".