Updating the Burn Center Referral Criteria: Results From the 2018 eDelphi Consensus Study
Bibliographic record
Abstract
Existing burn center referral criteria were developed several years ago, and subsequent innovations in burn care have occurred. Coupled with frequent errors in the estimation of extent of burn injury and depth by referring providers, patients are both over and under-triaged when the existing criteria are used to support patient care decisions. In the absence of compelling clinical trial data on appropriate burn patient triage, we convened a multidisciplinary panel of experts to execute an iterative eDelphi consensus process to facilitate a revision. The eDelphi process panel consisted of n = 61 burn stakeholders and experts and progressed through four rounds before reaching consensus on key clinical domains. The major findings are that 1) burn center consultation is strongly recommended for all patients with deep partial-thickness or deeper burns ≥ 10% TBSA burned, for full-thickness burns ≥ 5% TBSA burned, for children and older adults with specific dressing and medical needs, and for special burn circumstances including electrical, chemical, and radiation injuries; 2) smaller burns are ideally followed in burn center outpatient settings as soon as possible after injury, preferably without delays of a week or more; 3) frostbite, Stevens-Johnson syndrome/TENS, and necrotizing soft-tissue infection patients benefit from burn center treatment; and 4) telemedicine and technological solutions are of likely benefit in achieving this standard. Unlike the original criteria, the revised consensus-based guidelines create a framework promoting communication so that triage and treatment are specifically tailored to individual patient characteristics, injury severity, geography, and the capabilities of referring institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".