Cost-effectiveness in the management of Dupuytren’s contracture
Bibliographic record
Abstract
In Canada, Dupuytren's contracture is managed with partial fasciectomy or percutaneous needle aponeurotomy (PNA). Injectable collagenase will soon be available. The optimal management of Dupuytren's contracture is controversial and trade-offs exist between the different methods. Using a cost-utility analysis approach, our aim was to identify the most cost-effective form of treatment for managing Dupuytren's contracture it and the threshold at which collagenase is cost-effective. We developed an expected-value decision analysis model for Dupuytren's contracture affecting a single finger, comparing the cost-effectiveness of fasciectomy, aponeurotomy and collagenase from a societal perspective. Cost-effectiveness, one-way sensitivity and variability analyses were performed using standard thresholds for cost effective treatment ($50 000 to $100 000/QALY gained). Percutaneous needle aponeurotomy was the preferred strategy for managing contractures affecting a single finger. The cost-effectiveness of primary aponeurotomy improved when repeated to treat recurrence. Fasciectomy was not cost-effective. Collagenase was cost-effective relative to and preferred over aponeurotomy at $875 and $470 per course of treatment, respectively. In summary, our model supports the trend towards non-surgical interventions for managing Dupuytren's contracture affecting a single finger. Injectable collagenase will only be feasible in our publicly funded healthcare system if it costs significantly less than current United States pricing.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".