Burn-Related Violence Against Women in the United States: Findings From the ABA Burn Registry
Bibliographic record
Abstract
Violence against women is a global public health problem. Centers for Disease Control and Precention (CDC) data show 41% of US women have experienced intimate partner violence. Burn-related violence against women is an extremely confronting form of physical violence. The aim of this study was to describe the frequency, demographics, injury characteristics, and outcomes of women admitted to US burn centers who have experienced burn violence compared to those with accidental burn injuries. 2008-2018 data were comparative statistics were used to describe/compare groups. 54 523 women met study inclusion criteria. 956 (2%) experienced burn violence. Women who experienced burn violence had a younger median [IQR] age (36 [27,48] vs 47 [32,61], P < .0001), were Black/African American (44.5% vs 22.4%, P < .0001), were covered by Medicaid (38.8% vs 21.6%, P < .0001), had a higher median [IQR] %TBSA extent (6.0% [3,15.2] vs 3.0% [1,7.3], P < .0001), a higher proportion with third-degree burns (35.4% vs 28.9%, P < .0001), and a higher proportion with TBSA > 20% (18.2% vs 6.7%, P < .0001). Scald/flame injuries were the most frequent mechanism of injury. Women who experienced violence had a higher median [IQR] length of hospital stay (7.0 [2,18] vs 4.0 [1,11] days, P <.0001), Intensive Care Unit (ICU) stay (8.5 [2,27] vs 4 [2,13] days, P < .0001), and mortality rate (5.7% vs 4.3%, P < .04). The frequency of women who sustained burn violence appears small yet experience worse outcomes. Clinicians should be aware of these demographic/clinical characteristics to provide optimal care to this vulnerable population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".