Survivors Versus Nonsurvivors Postburn
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether a panel of common biomedical markers can be utilized as trajectories to determine survival in pediatric burn patients. BACKGROUND: Despite major advances in clinical care, of the more than 1 million people burned in the United States each year, more than 4500 die as a result of their burn injuries. The ability to predict patient outcome or anticipate clinical trajectories using plasma protein expression would allow personalization of clinical care to optimize the potential for patient survival. METHODS: A total of 230 severely burned children with burns exceeding 30% of the total body surface, requiring at least 1 surgical procedure were enrolled in this prospective cohort study. Demographics, clinical outcomes, and inflammatory and acute-phase responses (serum cytokines, hormones, and proteins) were determined at admission and at 11 time points for up to 180 days postburn. Statistical analysis was performed using a 1-way analysis of variance, the Student t test, χ test, and Mann-Whitney test where appropriate. RESULTS: Survivors and nonsurvivors exhibited profound differences in critical markers of inflammation and metabolism at each time point. Nonsurvivors had significantly higher serum levels of interleukin (IL)-6, IL-8, granulocyte colony-stimulating factor, monocyte chemoattractant protein-1, C-reactive protein, glucose, insulin, blood urea nitrogen, creatinine, and bilirubin (P < 0.05). Furthermore, nonsurvivors exhibited a vastly increased hypermetabolic response that was associated with increases in organ dysfunction and sepsis when compared with survivors (P < 0.05). CONCLUSIONS: Nonsurvivors have different trajectories in inflammatory, metabolic, and acute phase responses allowing differentiation of nonsurvivors from survivors and now possibly allowing novel predictive models to improve and personalize burn outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".