The role of genomics to identify biomarkers and signaling molecules during severe sepsis.
Bibliographic record
Abstract
Early strategies to diagnose, manage and predict outcome of sepsis are essential to further improve morbidity and mortality of sepsis. Whereas biomarkers have become mainstay in other fields of medicine, their clinical utility in sepsis remains generally much less proven and so biomarkers are much less used clinically. The Human Genome Project embellished genomics, transcriptomics, proteomics and metabolomics and continues to expand our knowledge of the genetic, gene expression, protein translational and metabolic discoveries that could lead to clinical biomarker tests related to sepsis thereby allowing insight into the disease as never seen before. We explore the genomic approach to biomarker identification and validation by reviewing pertinent studies related to the diagnosis (diagnostic biomarkers), prediction of response to therapies (predictive biomarkers) and (prognostic biomarkers) outcomes of sepsis.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".