A population-based evaluation of long-term outcomes following major burn injury
Bibliographic record
Abstract
Introduction: Advances in critical care and the regionalization of burn care have resulted in a significant decrease in mortality after burn injury such that most burn-injured patients are expected to survive. This thesis aimed to evaluate and characterize long-term outcomes following major burn injury. Methods: Using linked health administrative data, we identified adults who sustained major burn injury during 2003-2014. First, we evaluated temporal changes in the incidence and in-hospital mortality of burn injury. We then estimated five-year risk-adjusted rates and principal causes of emergency department visits and readmissions. A self-matched longitudinal cohort study was performed to compare rates of mental illness-related healthcare utilization in the 3 years before and after burn. Finally, a matched cohort study was performed to estimate 5-year mortality after burn. Results: The incidence of burn injury remained stable over time, and significant reductions in the overall in-hospital mortality rate occurred; the odds of death in 2010-2013 were significantly lower than 2003-2006 (odds ratio (OR) 0.39, 95% CI 0.25-0.61). In the five years after discharge from the index burn admission, 70% of patients had >1 emergency department (ED) visit, and 30% had an unplanned readmission. The principal cause of healthcare utilization was unintentional injury, followed by mental illness and respiratory disease. Patients who received burn center care had significantly lower rates of ED visits (rate ratio 0.64, 95% CI 0.56-0.73) and readmissions (hazard ratio 0.77, 95% CI 0.65-0.92). Burn injury was not associated with a higher rate of mental health visits compared to the pre-injury period (RR 0.97, 95% CI 0.78-1.20), but was associated with a two-fold increase in self-harm (RR 1.95 (95% CI 1.15-3.33). Five-year mortality after burn injury was 11%, compared to 4% among matched controls. The hazard ratio was greatest during the first year after discharge (HR 4.15, 95% CI 3.17-5.42). Conclusions: Burn injury confers a physical and psychological impact up to five years after discharge, associated with high rates of healthcare utilization and increased late mortality compared to matched controls. Further development of consolidated and focused burn care, and improved mental healthcare, offer an opportunity to improve outcomes after burn injury.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".