SCORTEN Accurately Predicts Mortality Among Toxic Epidermal Necrolysis Patients Treated in a Burn Center
Bibliographic record
Abstract
SCORTEN is a scoring system used to predict mortality in toxic epidermal necrolysis (TEN) patients. The accuracy of SCORTEN among TEN patients treated in burn centers has not been established. The purpose of this study was to assess the discriminative power and calibration of SCORTEN among TEN patients treated at an adult regional burn center. Retrospective analysis of a consecutive series of TEN patients was used to compare actual mortality with that predicted by SCORTEN. A standardized mortality ratio was obtained to compare the actual number of deaths to the predicted number based on SCORTEN. Discrimination was measured using the area under the receiver operator characteristic curve, and model fit (calibration) was measured using the Hosmer-Lemeshow goodness-of-fit statistic. A total of 61 adult patients were analyzed. The actual overall mortality rate of 29.5% was not significantly different than the mortality rate of 25.2% predicted by SCORTEN (standardized mortality ratio, 1.17; 95% confidence intervals, 0.695-1.853; P = .08). The area under the receiver operator characteristic curve was 0.82 and the Hosmer-Lemeshow statistic was 1.381 (P = .710). SCORTEN is an accurate scoring system for estimation of mortality among TEN patients treated in a burn center setting.
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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 | 0.008 |
| 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".