Fireworks and Seafood Boils: The Epidemiology of Burns in Louisiana
Bibliographic record
Abstract
Epidemiological trends provide a means for targeting efforts in burn prevention. There have been but few regional-specific studies concerning burns in the southern United States. This study describes burn injury trends experienced by a single burn center in Louisiana. We also investigate the temporal relationships of several activities informally known for having a high risk for burn injury among local providers. Data were retrospectively extracted from the records of all patients treated for burn injuries at the regional burn center from 2012 to 2018 in both inpatient and outpatient settings. Demographical data and burn injury characteristics were noted. A total of 6,498 patients were included (1,593 inpatient, 4,905 outpatient). The most common burn etiologies were scald and flame, with flame being associated with more severe injuries. Overall incidence was disproportionally high in males and children less than 4 years of age. Total incidence was highest in Caucasians, though African Americans held the highest annual incidence rate specific to this population. The most common situation at the time of burn injury involved the consumption or preparation of food or beverages. Significant variation was observed in the rates of different injury situations throughout the year. Notably, burns related to seafood, heating, and firework activity occurred more often during crawfish season, colder months, and the months of January and July, respectively. In addition to establishing preliminary trends, these data may be useful in guiding the development of future evidence-based prevention efforts to target the most detrimental burn injuries in this 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".