101 Predicting and Estimating Burn Outcomes: A Single Institution Analysis of over 4000 Cases
Bibliographic record
Abstract
Abstract Introduction Advances in burn care have improved patient outcomes, and independently validated indices, scores, 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. Methods A retrospective review of all burn patients (n = 5778) admitted to a quaternary provincial burn unit from 1973 to 2017, was conducted. Removal of blank and pediatric entries yielded 4622 independent cases. Goodness-of-fit models and multivariate logistic regression was performed. Burn predictors included %TBSA, Baux (classic, revised) index, Abbreviated Burn Severity Index, and Ryan score. Primary outcomes were mortality and LOS. Variables considered in the multivariate logistic regression included: diabetes, hypertension, smoking, obesity, alcohol use, drug abuse, full-thickness burn, ventilator support, and ICU referral. Results Multivariate logistic regression for mortality showed the classic Baux index to be a significant predictor for mortality (OR = 1.118, p < 0.001). Other predictors included male sex, ICU referral, diabetes, smoking, and alcoholism (OR = 1.96, 4.97, 2.38, 1.63, 1.98, all p < 0.05). Interestingly, hypertension had a protective effect (OR = 0.24, p < 0.013). Linear regression for LOS found %TBSA, ICU referral, alcoholism, age, male sex, significant. The area under the ROC curve for Baux index was 0.945. Conclusions The regressions show that burn mortality and LOS are best predicted with the Baux index. Hypertension may have a protective effect on burn outcomes and may be attributed to increased perfusion to the periphery. Goodness-of-fit models, although variable, tended to show tighter grouping in patients with TBSA >20%. LOS ratios prove to be useful benchmarks for burn units with TBSA >20%. Similar findings are preliminary found in the NBR, national burn repository, database. Applicability of Research to Practice LOS ratios and burn index scores prove to be valuable markers to predict burn outcomes. The results of this study will directly help the clinician make decisions and communicate clinical severity to patient and family members.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".