What do we know about managing Dupuytren’s disease cost-effectively?
Bibliographic record
Abstract
BACKGROUND: Dupuytren's disease (DD) is a common and progressive, fibroproliferative disorder of the palmar and digital fascia of the hand. Various treatments have been recommended for advanced disease or to retard progression of early disease and to prevent deterioration of the finger contracture and quality of life. Recent studies have tried to evaluate the clinical and cost-effectiveness of therapies for DD, but there is currently no systematic assessment and appraisal of the economic evaluations. METHODS: A systematic literature review was conducted, following PRISMA guidelines, to identify studies reporting economic evaluations of interventions for managing DD. Databases searched included the Ovid MEDLINE/Embase (without time restriction), National Health Service (NHS) Economic Evaluation Database (all years) and the National Institute for Health Research (NIHR) Journals Library) Health Technology Assessment (HTA). Cost-effectiveness analyses of treating DD were identified and their quality was assessed using the CHEERS assessment tool for quality of reporting and Phillips checklist for model evaluation. RESULTS: A total of 103 studies were screened, of which 4 met the study inclusion criteria. Two studies were from the US, one from the UK and one from Canada. They all assessed the same interventions for advanced DD, namely collagenase Clostridium histolyticum injection, percutaneous needle fasciotomy and partial fasciectomy. All studies conducting a cost-utility analysis, two implemented a decision analytic model and two a Markov model approach. None of them were based on a single randomised controlled trial, but rather synthesised evidence from various sources. Studies varied in their time horizon, sources of utility estimates and perspective of analysis. The overall quality of study reporting was good based on the CHEERS checklist. The quality of the model reporting in terms of model structure, data synthesis and model consistency varied across the included studies. CONCLUSION: Cost-effectiveness analyses for patients with advanced DD are limited and have applied different approaches with respect to modelling. Future studies should improve the way they are conducted and report their findings according to established guidance for conducting economic modelling of health care technologies. TRIAL REGISTRATION: The protocol was registered ( CRD42016032989 ; date 08/01/2016) with the PROSPERO international prospective register of systematic reviews.
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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 | 0.016 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".