Epidemiología del paciente gran quemado adulto en Chile: experiencia del Servicio de Quemados del Hospital de la Asistencia Pública de Santiago
Bibliographic record
Abstract
Background: Approximately 150 subjects per year suffer severe burns in Chile. Aim: To analyze sociodemographic/clinical features and outcomes of severely burned patients. Material and Methods: Retrospective cohort study of 936 patients aged 47 ± 20 years (66% males), admitted to the National Burn Center of Chile between 2006 and 2010. Sociodemographic/clinical and burn variables and outcomes were studied. Results: Mean total percentage of body surface area burned was 27 + 20%. A quarter of the patients had social features that could jeopardize rehabilitation. Fire was the burning agent in 73%, which along with electricity presented greater lethality (p < 0.01). Inhalation injury was diagnosed in 22% of the patients. Twenty eight percent of patients had impaired consciousness at the moment of the accident, leading to larger burns, higher incidence of inhalation injury and greater lethality. Lethality for severe, critical and exceptional survival groups was 8.4,37.7 and 70.4%, respectively. Conclusions: Severely burned patients in Chile are mainly males at working age. Fire is the main agent and 28% had impaired consciousness, which was associated with an increase in the severity of burns. Knowledge of the characteristics and outcomes of the patients is important to implement prevention and treatment strategies adjusted to the national reality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".