Perspective: Sepsis Biomarker Research Requires a Move to the Emergency Department and More Collaboration
Bibliographic record
Abstract
Despite years of research and multiple potential candidate biomarkers for sepsis, few have had sufficient sensitivity or specificity to be integrated into routine practice. There have been only 11 observational studies that have collected samples from patients presenting to the emergency department with suspected sepsis. This has resulted in gaps in the ability to accurately diagnose sepsis in patients presenting with infections and give an accurate prognosis for patients or their families. Recent work has shown the importance of immunothrombosis, particularly in the prognosis for patients admitted to the intensive care unit with sepsis. Significantly some of the most impactful markers are actually decreased. In this perspective we summarize the current sepsis biomarker literature, highlight the limitations, particularly in diagnosis, and suggest some strategies for moving this field forward.
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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 | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.017 | 0.028 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".