Determining clinically meaningful thresholds for innovative burn care products to reduce autograft: A US burn surgeon Delphi panel
Bibliographic record
Abstract
Reducing the amount of donor skin needed for definitive wound closure can improve outcomes in patients with severe burns. This Delphi Consensus Panel (DCP) aimed to achieve expert consensus on the percentage reduction in donor skin for autograft that constitutes a clinically meaningful benefit. A two-round DCP of fifteen US burn surgeons was conducted via a web-based survey platform. Fourteen panelists (93.3%) completed both rounds. In Round 2, consensus, defined as ≥70% agreement, was achieved for five of the seven consensus statements. All panelists agreed that a clinically meaningful reduction in the amount of donor skin required would facilitate wound management and decrease donor site morbidity experienced by patients. Furthermore, based on three treatment scenarios, consensus was achieved for a clinically meaningful reduction in the amount of donor skin required for autograft for the adult population in deep partial-thickness and full-thickness burns. Findings from this DCP indicate that an innovative cellular and/or tissue product that would reduce the needed amount of donor skin, by the identified thresholds, has the potential to improve the outcomes for patients with severe burn injuries in a meaningful way.
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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 | 0.069 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".