Pharmacogenomics in sepsis and septic shock
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
Abstract The promise of pharmacogenomics lies in the ability to tailor patient therapies based on a genetic risk profile. This risk profile may be based on known literature single nucleotide polymorphisms or it may be encompassed by genome‐wide scans for risk alleles. In either case, it is the polymorphisms of a patient that will be the focus in the search for better individualized therapies. The risk profile for sepsis has been indicated to be heavily influenced by various alleles of candidate genes. Several likely genes have been investigated, some in depth, while others have received less attention. We review the case for genetic susceptibility to sepsis and summarize the literature to date investigating the use of single nucleotide polymorphisms and haplotypes in the search for risk profiles. We discuss the methods in use for structuring associations between complex diseases and genomics. Finally, we summarize the literature to date dealing with risk polymorphisms in candidate genes including tumor necrosis factor (TNF)‐α, lymphotoxin‐α (LTA), interleukin (IL)‐6 and ‐10, as well as others. Drug Dev Res 64:181–194, 2005. © 2005 Wiley‐Liss, Inc.
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 | 0.002 | 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) | 0.001 | 0.002 |
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