The FLAMES Score Accurately Predicts Mortality Risk in Burn Patients
Bibliographic record
Abstract
BACKGROUND: The purposes of this study were to determine current mortality predictors in our thermally injured population, to develop and validate a new mortality predictive score, and to compare its predictive ability with those of the acute physiology and chronic health evaluation II (APACHE II) score, multiple organ dysfunction (MOD) score, and two burn-specific mortality predictive scores. METHODS: A retrospective chart review of acute thermally injured (flame or scald) patients admitted during a 12-year period (1991-2003) to an adult regional burn center was performed. Patients admitted between January 1991 and February 1995 (derivation population) were included in the development of a mortality risk predictive score along with the patient's APACHE II score, MOD score, Smith's score, and the Age-Risk score. The new mortality risk predictive score was validated in a separate group of thermally injured patients (validation population) admitted to the same burn center between March 1995 and December 2003. RESULTS: Of 1,439 acute thermally injured patients admitted between 1991 and 2003, 96 (7%) were excluded because they received comfort measures only. Of the remaining 1,343 patients, 378 (28%) were included in the mortality risk score derivation, and 965 (72%) in its validation. In the derivation group, there were 260 (69%) flame burns and 118 (31%) scald burns, and 35 (9%) patients died in hospital. Increased age, day 1 APACHE II score, percent partial-thickness burn, percent full-thickness burn, and sex were the strongest predictors of mortality. With these factors, we developed the FLAMES score (Fatality by Longevity, APACHE II score, Measured Extent of burn, and Sex), which had an area under the receiver operating characteristic curve of 0.97 that was better (p < 0.001) than those of the APACHE II score (0.91), MOD score (0.89), Smith's score (0.93), and the Age-Risk score (0.94). The FLAMES score was tested in the validation population and the area under the receiver operating characteristic curve = 0.93 was better (p < 0.001) than those of the APACHE II score (0.83), Smith's score (0.91), and the Age-Risk score (0.72). CONCLUSION: The ability of the FLAMES score in predicting hospital mortality risk was validated in a regional burn center population.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".