American Burn Association Consensus Statements
Bibliographic record
Abstract
Nicole S. Gibran, MD, FACS* *From the University of Washington Medicine Regional Burn Center, University of Washington Medicine Department of Surgery, Seattle, Washington. See under consensus statements for author affiliations. The 2012 ABA burn quality consensus conference was underwritten in part by unrestricted educational grants from Molnlycke Health Care and Baxter Health Care. Address correspondence to Nicole S. Gibran MD, FACS, UW Medicine Regional Burn Center, UW Medicine Department of Surgery, Seattle, Washington 98104. Quality is generally recognized to have three components: structure, process, and outcomes. The American Burn Association (ABA) has a long history of trying to improve quality of care for patients with burn injuries. Since its establishment by Dr. Irving Feller in the 1970s,1 the National Burn Information Exchange, a nascent database relying on punch cards submitted by participating burn centers, has worked to drive quality improvement, regional healthcare planning, resource allocation, and research and prevention. Over time, this project evolved into the voluntary ABA project, the National Burn Repository (NBR), which now reports on incidence, etiology, and acute outcomes. The 2011 NBR Summary Report included more than 160,000 submissions from burn centers in the United States and Canada (and in 2010, Sweden). Like all data repositories, it has imperfections including missing data fields, but the ABA has initiated measures to incorporate a validator to minimize inconsistent data.
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 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 | 0.082 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.047 | 0.036 |
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".