Establishment of a core outcome set for burn care research: development and international consensus
Bibliographic record
Abstract
Objective: To develop a core outcome set for international burn research. Design: Development and international consensus, from April 2017 to November 2019. Methods: Candidate outcomes were identified from systematic reviews and stakeholder interviews. Through a Delphi survey, international clinicians, researchers, and UK patients prioritised outcomes. Anonymised feedback aimed to achieve consensus. Pre-defined criteria for retaining outcomes were agreed. A consensus meeting with voting was held to finalise the core outcome set. Results: Data source examination identified 1021 unique outcomes grouped into 88 candidate outcomes. Stakeholders in round 1 of the survey, included 668 health professionals from 77 countries (18% from low or low middle income countries) and 126 UK patients or carers. After round 1, one outcome was discarded, and 13 new outcomes added. After round 2, 69 items were discarded, leaving 31 outcomes for the consensus meeting. Outcome merging and voting, in two rounds, with prespecified thresholds agreed seven core outcomes: death, specified complications, ability to do daily tasks, wound healing, neuropathic pain and itch, psychological wellbeing, and return to school or work. Conclusions: This core outcome set caters for global burn research, and future trials are recommended to include measures of these outcomes.
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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 | 0.481 | 0.473 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.011 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".