Trajectories of Survivors and Non-Survivors Post-burn Injury
Bibliographic record
Abstract
OBJECTIVE: To gain insights into the systemic response after burn injury, we aimed to characterize the phases of inflammatory and metabolic trajectories in survivors and non-survivors. BACKGROUND: Survival after burn injuries has improved over the past few decades. However, a large proportion of the patients do not survive. METHODS: This was a single-center cohort study. We included patients (aged ≥ 18 years) with burn injuries (≥ 10% total body surface area; TBSA) admitted to our provincial burn center. Clinical outcomes, laboratory measures, and inflammatory biomarkers were compared among survivors, early non-survivors (died ≤4 days post-injury), and late non-survivors (died ≥5 days post-injury). RESULTS: We studied 872 patients with a median age of 49 (Interquartile Range, IQR: 35-63) years and a median percent TBSA burn of 19% (IQR: 13-34) TBSA burn for survivors (n=705; 81%), early non-survivors (n=99; 11%), and late non-survivors (n=68; 8%). The median ages were 46 (IQR: 33-59) years for survivors, 62 (IQR: 46-73) years for early non-survivors, and 67 (IQR: 54-76) years for late non-survivors (P<0.0001). The median % TBSA burn was 17 (IQR: 13-26) for survivors, 67 (IQR: 43-88) for early non-survivors, and 27 (IQR: 18-44) for late non-survivors (P<0.0001). Non-survivors exhibited significantly elevated biomarkers compared to survivors, with distinct metabolic and inflammatory profiles, including increased IL-1β, IL-8, TNF-α, and IL-10. Late non-survivors experienced higher complication rates (P<0.01), with significant differences in inflammatory and metabolic responses over time. CONCLUSIONS: Survivors and non-survivors showed distinct post-injury inflammatory and metabolic responses. Identifying the relationship between 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 between early and late non-survivors could allow for the prediction and identification of patients at risk of mortality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".