Bibliographic record
Abstract
Sapru et al. show in this issue of Critical Care that variants of thrombomodulin and the endothelial protein C receptor, but not protein C, are associated with mortality and organ dysfunction (ventilation-free and organ failure-free days) in ARDS. Hundreds of gene variants have been found prognostic in sepsis. However, none of these prognostic genomic biomarkers are used clinically. Predictive biomarker discovery (pharmacogenomics) usually follows a candidate gene approach, utilizing knowledge of drug pathways. Pharmacogenomics could be applied to enhance efficacy and safety of drugs used for treatment of sepsis (e.g., norepinephrine, epinephrine, vasopressin, and corticosteroids). Pharmacogenomics can enhance drug development in sepsis, which is very important because there is no approved drug for sepsis. Pharmacogenomics biomarkers must pass three milestones: scientific, regulatory, and commercial. Huge challenges remain but great opportunities for pharmacogenomics of sepsis are on the horizon.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.026 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 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".