American Burn Association Strategic Quality Summit 2022: Setting the Direction for the Future
Bibliographic record
Abstract
The American Burn Association (ABA) hosted a Burn Care Strategic Quality Summit (SQS) in an ongoing effort to advance the quality of burn care. The goals of the SQS were to discuss and describe characteristics of quality burn care, identify goals for advancing burn care, and develop a roadmap to guide future endeavors while integrating current ABA quality programs. Forty multidisciplinary members attended the two-day event. Prior to the event, they participated in a pre-meeting webinar, reviewed relevant literature, and contemplated statements regarding their vision for improving burn care. At the in-person, professionally facilitated Summit in Chicago, Illinois, in June 2022, participants discussed various elements of quality burn care and shared ideas on future initiatives to advance burn care through small and large group interactive activities. Key outcomes of the SQS included burn-related definitions of quality care, avenues for integration of current ABA quality programs, goals for advancing quality efforts in burn care, and work streams with tasks for a roadmap to guide future burn care quality-related endeavors. Work streams included roadmap development, data strategy, quality program integration, and partners and stakeholders. This paper summarizes the goals and outcomes of the SQS and describes the status of established ABA quality programs as a launching point for futurework.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".