Cost-utility analysis of a polylactic acid matrix versus a collagen dressing for the closure of diabetic foot ulcers
Bibliographic record
Abstract
Background: Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, often requiring the use of advanced care products, which may have high upfront costs. With the hypothesis that this approach leads to faster healing, reduced costs, and better quality-adjusted life years (QALY), this study evaluates the economic outcomes, including cost-effectiveness and cost-benefit, of an alloplastic polylactic acid (PLA) dermal matrix and compares it to collagen dressings in managing DFUs. Methods: This cost-utility analysis was based on a randomized controlled trial involving patients with DFUs treated with either PLA matrices or collagen dressings, alongside standard wound care. Data on wound healing, cost of care, and QALY were collected over the whole duration of the trial (31-week period). We conducted a cost-benefit analysis by quantifying the monetary impact of reduced time-to-heal and avoided healthcare utilization. Additionally, we performed a cost-utility analysis using QALYs to capture patient-centered benefits. Results: < 0.001). Sensitivity analyses confirmed the cost-effectiveness of PLA even when home healthcare costs were excluded. Cost-benefit analysis also demonstrated higher QALY in the PLA treated group. Conclusion: PLA dermal matrices provide a cost-effective alternative to collagen dressings, promoting faster wound closure, improved quality of life, and reduced healthcare costs. These results support the adoption of PLA as a preferred treatment for DFUs.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".