Predicting and Estimating Burn Outcomes: An Institutional Analysis of 4622 Cases
Bibliographic record
Abstract
Advances in burn care have improved patient outcomes, and independently validated indices and predictors of burn outcomes warrant re-evaluation. The purpose of this study is to consolidate predictors of burn outcomes and determine the factors that significantly contribute to length-of-stay (LOS) and mortality. A retrospective review was conducted of all burn patients (n = 5778) admitted to a quaternary provincial burn unit from 1973 to 2017. Our inclusion criteria yielded 4622 independent cases. Multivariate linear and logistic regression models were generated, and area-under-receiver-operator-curve (AUROC) analysis was performed. Burn predictors included %TBSA, Baux (classic and revised) index, Abbreviated Burn Severity Index (ABSI), and Ryan score. Primary outcomes were mortality and LOS. Multivariate logistic regression for mortality showed the Baux index to be the best predictor for mortality (OR = 1.11, P < 0.001). The AUROC for Baux index was 0.95. With regard to LOS, ABSI was the best predictor for LOS (P < 0.001). ICU stay, ventilator use, alcoholism, and age are significantly associated with increased LOS. Interestingly, hypertension had a protective effect for LOS (P < 0.01) and trended towards a protective effect in mortality. Lethal score 50% (LS50) improved over the study period. The regressions show that burn mortality and LOS are best predicted with the Baux index and ABSI, respectively. Hypertension may have a protective effect on burn outcomes and may be attributed to increased perfusion to the periphery. These predictive scores are useful in determining institutional outcomes in burn surgery. Objective benchmarking of improvement in burn care outcomes can be established using LS50 trends.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".