Biomarkers of sepsis: time for a reappraisal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Sepsis biomarkers can have important diagnostic, therapeutic, and prognostic functions. In a previous review, we identified 3370 references reporting on 178 different biomarkers related to sepsis. In the present review, we evaluate the progress in the research of sepsis biomarkers. METHODS: Using the same methodology as in our previous review, we searched the PubMed database from 2009 until September 2019 using the terms "Biomarker" AND "Sepsis." There were no restrictions by age or language, and all studies, clinical and experimental, were included. RESULTS: We retrieved a total of 5367 new references since our previous review. We identified 258 biomarkers, 80 of which were new compared to our previous list. The majority of biomarkers have been evaluated in fewer than 5 studies, with 81 (31%) being assessed in just a single study. Apart from studies of C-reactive protein (CRP) or procalcitonin (PCT), only 26 biomarkers have been assessed in clinical studies with more than 300 participants. Forty biomarkers have been compared to PCT and/or CRP for their diagnostic value; 9 were shown to have a better diagnostic value for sepsis than either or both of these biomarkers. Forty-four biomarkers have been evaluated for a role in answering a specific clinical question rather than for their general diagnostic or prognostic properties in sepsis. CONCLUSIONS: The number of biomarkers being identified is still increasing although at a slower rate than in the past. Most of the biomarkers have not been well-studied; in particular, the clinical role of these biomarkers needs to be better evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 it