Measurement of central nervous system related biomarkers in plasma of burn patients
Bibliographic record
Abstract
BACKGROUND: Burn injury produces a complex biological response across multiple organs and biological systems. Nonetheless, current understanding regarding the neurologic response to burn injury is limited. Research suggests that disruption of the blood-brain barrier may play a role in central nervous system (CNS) damage after burn trauma. As such, the purpose of this study was to investigate systemic circulating biomarkers, frequently associated with neuronal injury, to gain an understanding of their relationship to burn injury severity. METHODS: Blood from 56 patients admitted to the burn intensive care units was taken within 24 hours and analyzed for four CNS-related biomarkers in plasma (i.e., ubiquitin C-terminal hydrolase L1, tau protein, glial fibrillary acidic protein, and neurofilament light). Clinical information regarding demographics, burn severity, and health outcomes was also obtained. RESULTS: We observed that increased burn severity, as measured by total burn surface area (TBSA), was significantly associated with increased ubiquitin C-terminal hydrolase L1, neurofilament light, and tau. Glial fibrillary acidic protein was not associated with burn severity. In a predictive model of days spent in the hospital after injury, the accuracy of the four CNS-related biomarkers was only improved by 1% when TBSA was included (i.e., 38.3% accuracy with only biomarkers vs. 39.4% accuracy with biomarkers and TBSA). CONCLUSIONS: Overall, findings from this novel study highlight an association between burn injury severity and CNS-related biomarkers, thereby providing a foundation for future studies to explore both potential mechanisms associated with burn-related neurologic damage and associated functional impairments.
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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 | 0.002 |
| 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".