Clinical utility of biomarkers of endothelial activation in sepsis-a systematic review
Bibliographic record
Abstract
INTRODUCTION: A strong biologic rationale exists for targeting markers of endothelial cell (EC) activation as clinically informative biomarkers to improve diagnosis, prognostic evaluation or risk-stratification of patients with sepsis. METHODS: The objective was to review the literature on the use of markers of EC activation as prognostic biomarkers in sepsis. MEDLINE was searched for publications using the keyword 'sepsis' and any of the identified endothelial-derived biomarkers in any searchable field. All clinical studies evaluating markers reflecting activation of ECs were included. Studies evaluating other exogenous mediators of EC dysfunction and studies of patients with malaria and febrile neutropenia were excluded. RESULTS: Sixty-one studies were identified that fulfilled the inclusion criteria. Overall, published studies report positive correlations between multiple EC-derived molecules and the diagnosis of sepsis, supporting the critical role of EC activation in sepsis. Multiple studies also reported positive associations for mortality and severity of illness, although these results were less consistent than for the presence of sepsis. Very few studies, however, reported thresholds or receiver operating characteristics that would establish these molecules as clinically-relevant biomarkers in sepsis. CONCLUSIONS: Multiple endothelial-derived molecules are positively correlated with the presence of sepsis in humans, and variably correlated to other clinically-important outcomes. The clinical utility of these biomarkers is limited by a lack of assay standardization, unknown receiver operating characteristics and lack of validation. Additional large-scale prospective clinical trials will be required to determine the clinical utility of biomarkers of endothelial activation in the management of patients with sepsis.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".