5 Trajectories of Survivors versus Non-Survivors Post-Burn Injury
Bibliographic record
Abstract
Survival post-burn injury has improved over the past few decades. However, there are still a large proportion of patients that do not survive. Survival can be affected by the complexity of the burn injury itself, pre-existing medical conditions, and complications. The purpose of this study was to characterize the phases of inflammatory and metabolic trajectories in survivors and non-survivors. Furthermore, we compared the trajectories for early and late death in non-survivors. We conducted a cohort study in patients (aged ≥ 18 years) with a burn injury admitted to our provincial burn centre. Blood samples, clinical indicators, and metabolic markers were selected based on time post-injury as follows: 0–1, 2–4, 5–10, 11–18, 19–28, ≥ 29 days. The inflammatory profile included IL-1β, interferon-γ, IL-1 receptor antagonist, IL-6, IL-10, IL-8, TNF-α, GM-CSF, and MCP-1. Hypermetabolism was assessed by resting energy expenditure. Clinical outcomes included organ biomarkers, morbidities, and hospital length of stay. Groups were compared using Student’s t-test, Mann-Whitney U, Fisher’s exact, and χ2 test as appropriate; P value of <0.05 considered statistically significant. Multivariable regression was used to model the association between survivors and non-survivors, adjusting for patient and injury characteristics. We studied 1,749 patients, mean age 47 ± 18 years and 33 ± 13% TBSA burn, with 1,623 survivors and 126 non-survivors. Demographics and injury characteristics were significantly different among survivors and non-survivors: mean age was 45 ± 18 versus 62 ± 18 (p<0.0001), proportion of inhalation injury 206 (13%) versus 73 (58%) (p<0.0001), and 11 ± 12% versus 48 ± 30% TBSA burn (p<0.0001) respectively. Of the non-survivors, 73 (58%) of died within the first four days of injury. After adjustment for patient and injury characteristics, organ biomarkers including creatinine, BUN, ALP, ALT, AST, bilirubin, lactate, and pH; and cytokines IL-6, IL-8, IL-10, MCP-1, TNF-α, G-CSF, and MCP-1 were significantly higher in non-survivors (p<0.05). Notably, these differences were further augmented among early and late non-survivors. Survivors and non-survivors express distinct inflammatory and metabolic responses post-injury. Identifying the relationship of concomitant immune activation and suppression among survivors and non-survivors may improve patient outcomes by defining and altering inflammatory trajectories. Elucidating the differences in trajectories among early and late non-survivors could better allow for prediction and identification of patients at risk to die.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".