The 2023 American Burn Association Research and Advocacy Summit: Our Roadmap
Bibliographic record
Abstract
Research is one of the American burn association's (ABA) strategic priorities. Advocacy is required not only to promote burn research, but also, the ABA's other strategic priorities (Prevention, Quality, and Education). The ABA convened a two-day Research and Advocacy (R&A) Summit in September 2023, to develop a roadmap for the organization's R&A efforts. The in-person summit identified fourteen key R&A initiatives. A multidisciplinary workgroup then developed strategies to achieve each initiative. The initiatives and strategies were then approved by the ABA's Board of Trustees as our organization's roadmap for R&A. The next task will be to implement the initiatives. This will require not only oversight from the ABA's Board of Trustees, but also effort from and collaboration between several of the ABA's committees and panels, including the burn science advisory panel, the research committee, the prevention committee, The governmental affairs committee, The organization and delivery of burn care committee, the quality and burn registry committee, the ad hoc Coding Committee, and the ABA's Central Office.
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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 | 0.048 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.022 | 0.021 |
| Insufficient payload (model declined to judge) | 0.101 | 0.054 |
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".