Design, conduct, and analysis of a multicenter, pharmacogenomic, biomarker study in matched patients with severe sepsis treated with or without drotrecogin Alfa (activated)
Bibliographic record
Abstract
BACKGROUND: A genomic biomarker identifying patients likely to benefit from drotrecogin alfa (activated) (DAA) may be clinically useful as a companion diagnostic. This trial was designed to validate biomarkers (improved response polymorphisms (IRPs)). Each IRP (A and B) contains two single nucleotide polymorphisms that were associated with a differential DAA treatment effect. METHODS: DAA is typically given to younger patients with greater disease severity; therefore, a well-matched control group is critical to this multicenter, retrospective, controlled, outcome-blinded, genotype-blinded trial. Within each center, DAA-treated patients will be matched to controls treated within 24 months of each other taking into account age, APACHE II, cardiovascular, respiratory, renal, and hematologic dysfunction, mechanical ventilation status, medical/surgical status, and infection site. A propensity score will estimate the probability that a patient would have received DAA given their baseline characteristics. Two-phase data transfer will ensure unbiased selection of matched controls. The first transfer will be for eligibility and matching data and the second transfer for outcomes and genotypic data. The primary analysis will compare the effect of DAA in IRP + and IRP - groups on in-hospital mortality through day 28. DISCUSSION: A design-based approach matching DAA-free to DAA-treated patients in a multicenter study of patients who have severe sepsis and high risk of death will directly compare control to DAA-treated groups for mortality by genotype. Results, which should be available in 2012, may help to identify the group of patients who would benefit from DAA and may provide a model for future investigation of sepsis therapies.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".