571 Ethnicity and Etiology in Burn Patients for the American Burn Association National Burn Repository (NBR)
Bibliographic record
Abstract
Abstract Introduction The purpose of this study was to analyze data from the American Burn Association National Burn Repository (NBR) with particular focus on patient ethnicity and burn etiology. We hypothesize that burn etiology, severity and other characteristics will be significantly different between differing ethnic groups throughout the database. This information can be used to augment burn prevention strategies by targeting at risk ethnic groups. Methods Data on burn patients was derived from the American Burn Association National Burn Registry including all burn entries for a 10 year period (2009 to 2018) from 46 burn centers. The ethnic categories for this study were White, Black, Hispanic, Indigenous and Asian. The study also involved analysis of patient demographics, burn severity, context of injury, and hospital course. Results White patients were the largest group (64.0%), had the highest proportion of flame injury (53.1%) and shared the highest mortality rate with indigenous patients (3.1% compared with 2.6% for all other ethnicities). Black individuals (22.2%) had higher rates of scald burns (53.2%), the shortest average hospital stay (16.8 days) and along with indigenous patients the highest rates of assault/abuse (2.0% and 1.9% respectively). Hispanic patients (10.0%) had more scald burns (47%), the largest proportion of men (66.5%), the highest incident of work-related injuries (18.0%) and the largest average TBSA at 10.1%. Asian patients (2.7%) had the largest proportion of scald injury (63.1%) and the smallest proportion of male patients (54.5%). Indigenous patients (1.1%) had higher rates of flame burns, suffered full thickness burns at the highest rate (32.8%), had the longest average stays in hospital (21) and the ICU (15) and had the highest rates of blanks for data entry. Conclusions This study found multiple significant differences in burn populations when compared by ethnicity. We have found that the indigenous population suffered full thickness burns at the highest rate and have had the longest average hospital stay as well average ICU stay. We have also had the unexpected finding of higher rates of unknowns in the indigenous population which may reflect racial bias at an institutional level nationally.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".