Bibliographic record
Abstract
BACKGROUND: A complex network of biological mediators underlies the clinical syndrome of sepsis. The nonspecific physiologic criteria of sepsis syndrome or the systemic inflammatory response syndrome do not adequately identify patients who might benefit from either conventional anti-infective therapies or from novel therapies that target specific mediators of sepsis. Validated biomarkers of sepsis may improve diagnosis and therapeutic decision making for these high-risk patients. OBJECTIVES: To develop a methodologic framework for the identification and validation of biomarkers of sepsis. METHODS: A small group meeting of experts in clinical epidemiology, biomarker development, and sepsis clinical trials; selective narrative review of the biomarker literature. RESULTS: The utility of a biomarker is a function of the degree to which it adds value to the available clinical information in the domains of screening, diagnosis, risk stratification, and monitoring of the response to therapy. We identified needs for greater standardization of biomarker methodologies, greater methodologic rigor in biomarker studies, wider integration of biomarkers into clinical studies (in particular, early phase studies), and increased collaboration among investigators, pharmaceutical industry, biomarker industry, and regulatory agencies. CONCLUSIONS: Biomarkers promise to transform sepsis from a physiologic syndrome to a group of distinct biochemical disorders. This transformation could aid therapeutic decision making, and hence improve the prognosis for patients with sepsis, but will require an unprecedented degree of systematic investigation and collaboration.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".