Management of severe thermal burns in the acute phase in adults and children
Bibliographic record
Abstract
OBJECTIVES: To provide recommendations to facilitate the management of severe thermal burns during the acute phase in adults and children. DESIGN: A committee of 20 experts was asked to produce recommendations in six fields of burn management, namely, (1) assessment, admission to specialised burns centres, and telemedicine; (2) haemodynamic management; (3) airway management and smoke inhalation; (4) anaesthesia and analgesia; (5) burn wound treatments; and (6) other treatments. At the start of the recommendation-formulation process, a formal conflict-of-interest policy was developed and enforced throughout the process. The entire process was conducted independently of any industry funding. The experts drew up a list of questions that were formulated according to the PICO model (Population, Intervention, Comparison, and Outcomes). Two bibliography experts per field analysed the literature published from January 2000 onwards using predefined keywords according to PRISMA recommendations. The quality of data from the selected literature was assessed using GRADE® methodology. Due to the current paucity of sufficiently powered studies regarding hard outcomes (i.e. mortality), the recommendations are based on expert opinion. RESULTS: The SFAR guidelines panel generated 24 statements regarding the management of acute burn injuries in adults and children. After two scoring rounds and one amendment, strong agreement was reached for all recommendations. CONCLUSION: Substantial agreement was reached among a large cohort of experts regarding numerous strong recommendations to optimise the management of acute burn injuries in adults and children.
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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 | 0.025 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".