Who Gets Burned in Brazil?
Bibliographic record
Abstract
Burns are preventable injuries that still represent a relevant public health issue. The identification of risk factors might contribute to the development of specific preventive strategies. Data of patients admitted at the Hospital due to acute burn injuries from May 2017 to December 2019, was extracted manually from medical records. The population was analyzed descriptively, and differences between groups were tested using the appropriate statistical test. The study population consisted of 370 patients with burns admitted to the Hospital burn unit during the study period. The majority of the patients were males (257/370, 70%), median age was 33 (IQR:18-43), median TBSA% was 13 (IQR 6.35-21.5 and range 0-87.5%), and 54% of patients had full-thickness burns (n = 179). Children younger than 13 years old represented 17% of the study population (n = 63), 60% of them were boys (n = 38), and scalds was the predominant mechanism of burn injury (n = 45). No children died, however 10% of adults did (n = 31). Self-inflicted burns were observed in 16 adults (5%), of whom 6 (38%) died during admission, however self-inflicted burns were not observed in children. Psychiatric disorders and substance misuse were frequent in this subgroup. White adults male from urban areas who had not completed primary school degree were the major risk group for burns. Smoking and alcohol misuse were the most frequent comorbidities. Accidental domestic flame burns were the predominant injuries in the adult population and scalds in the pediatric.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".