Lipidomic analysis enables prediction of clinical outcomes in burn patients
Bibliographic record
Abstract
Recent discoveries have highlighted the novel metabolic functions of adipose tissue in enhancing hypermetabolism after trauma. As the exact function and expression profiles of serum lipids and free fatty acids (FFA) are essentially unknown, we determined the lipidomic expression profile after burn in correlation to clinical outcomes to identify important lipid mediators affecting post-burn outcomes. We conducted a prospective cohort study with 46 adult burn patients and 5 healthy controls at the Ross Tilley Burn Center in Toronto, Canada. Patients were stratified based on major demographic and clinical variables, including age, burn severity, mortality, and sepsis. Serum FFAs and inflammatory markers were measured during acute hospital stay. We found that FFAs were acutely elevated post-burn and returned to baseline over time. Greater burn severity and age were associated with an impaired acute response in unsaturated FFAs and pro-inflammatory cytokines. Elevations in saturated and mono-unsaturated FFAs correlated significantly to increased mortality. In summary, persistent elevation of unsaturated lipids was associated with a functionally altered inflammatory-immunological milieu and worse clinical outcomes. The present lipidomic analysis indicates profound alterations in the lipid profile after burn by characterizing key lipids as potential diagnostic and outcome indicators in critically injured patients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".