P90. Cost-Effective Care for Massive Burns
Bibliographic record
Abstract
PURPOSE: Massive burns are challenging to treat surgically due to limited donor skin. Autologous epidermal grafts such as cultured epidermal autografts (CEAs) and spray keratinocyte suspensions are successful strategies for wound closure when donor sites are limited. There are no investigations to date that describe the differential outcomes or costs between these competing strategies. Cost-effectiveness analysis is required to guide payers, hospitals, and policy makers in determinations of care. METHODS: A cost-utility analysis compared CEAs with spray keratinocytes in adult burn patients with ≥50% total body surface area deep partial thickness burns. Hybrid Monte Carlo simulation and Markov modeling studied cost-utility from the payer perspective. Model utilities were derived from the Vancouver Scar Scale (VSS) with 1 as the best outcome and 0 as the worst outcome. Deterministic and probabilistic sensitivity analyses were performed varying all model parameters. RESULTS: CEAs achieved successful wound closure in 73% of simulations compared to 88% for spray keratinocytes. Compared to treatment with CEAs, treatment with spray keratinocytes resulted in cost savings of $254,743, with no compromise in overall utility of treatment based on VSS. As such, spray keratinocyte treatment was the dominant strategy. This finding was robust upon sensitivity analyses. CONCLUSION: Spray keratinocyte suspensions were a dominant strategy to CEAs (i.e. cost saving without compromising utility). Payers and providers should consider the cost utility of spray keratinocytes as the dominant treatment strategy for massive burn epidermal grafting. CEA manufactures may want to consider cost reductions to be more economically competitive with alternative strategies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.002 |
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".