Predicting Prognosis in Thermal Burns With Associated Inhalational Injury: A Systematic Review of Prognostic Factors in Adult Burn Victims
Bibliographic record
Abstract
Burn injuries are a significant problem with high associated morbidity and mortality. Those associated with inhalational trauma (IHT) may be associated with higher mortality, but studies on prognosis are small and underpowered. This study was designed to identify prognostic factors that increase the risk of death, to quantify this risk, and to identify existing prognostic models. An electronic search of English-language publications that identify prognostic risk factors in thermal burns including IHT was carried out. Each article was reviewed systematically, and data extraction, quality assessment, and summarization of the articles were performed. Thirteen articles that met the inclusion/exclusion criteria of this study were reviewed. Overall, the mortality rate among burn patients in this review was 13.9% (4-28.3%), with the mortality rate among those with IHT being 27.6% (7.8-28.3%). Those studies with multivariate analyses identified increasing %TBSA, presence of IHT, and increasing age as the strongest predictors for mortality in this patient population. It seems that %TBSA, presence of IHT, and age are the best predictors of mortality among the current published literature on burn prognosis.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".