Sympathoadrenal Activation is Associated with Acute Traumatic Coagulopathy and Endotheliopathy in Isolated Brain Injury
Bibliographic record
Abstract
BACKGROUND: Acute coagulopathy after traumatic brain injury (TBI) involves a complex multifactorial hemostatic response that is poorly characterized. OBJECTIVES: To examine early posttraumatic alterations in coagulofibrinolytic, endothelial, and inflammatory blood biomarkers in relation to sympathetic nervous system (SNS) activation and 6-month patient outcomes, using multivariate partial least-squares (PLS) analysis. PATIENTS AND METHODS: A multicenter observational study of 159 adult isolated TBI patients admitted to the emergency department at an urban level I trauma center, was performed. Plasma concentrations of 6 coagulofibrinolytic, 10 vascular endothelial, 19 inflammatory, and 2 catecholamine biomarkers were measured by immunoassay on admission and 24 h postinjury. Neurological outcome at 6 months was assessed using the Extended Glasgow Outcome Scale. PLS-discriminant analysis was used to identify salient biomarker contributions to unfavorable outcome, whereas PLS regression analysis was used to evaluate the covariance between SNS correlates (catecholamines) and biomarkers of coagulopathy, endotheliopathy, and inflammation. RESULTS: Biomarker profiles in patients with an unfavorable outcome displayed procoagulation, hyperfibrinolysis, glycocalyx and endothelial damage, vasculature activation, and inflammation. A strong covariant relationship was evident between catecholamines and biomarkers of coagulopathy, endotheliopathy, and inflammation at both admission and 24 h postinjury. CONCLUSIONS: Biomarkers of coagulopathy and endotheliopathy are associated with poor outcome after TBI. Catecholamine levels were highly correlated with endotheliopathy and coagulopathy markers within the first 24 h after injury. Further research is warranted to characterize the pathogenic role of SNS-mediated hemostatic alterations in isolated TBI.
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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 | 0.000 | 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) | 0.001 | 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".