Bibliographic record
Abstract
Genetic variation has been shown to play a large role in determining susceptibility to and outcome of such complex diseases as sepsis. There is a much higher heritability of death due to infection than death due to cancer or heart disease. More than 8 million single nucleotide polymorphisms (SNPs) have been detected in the human genome, and there is very little understanding of their effect on gene expression and protein function. The use of haplotypes, which are inherited sets of linked SNPs, as the unit of genetic variation in association studies and the marking of these haplotypes with unique "tag SNPs" may help to narrow down the search for causal SNPs. Future studies must be large (thousands of patients) and must be carefully designed to avoid false associations resulting from ethnic differences in genotype frequencies and disease prevalence in order to find true, reproducible associations between genotype and phenotype. Functional studies and careful characterization of intermediate phenotypes must be done to lend biological plausibility to genotype-phenotype associations. Examination of the association between genetic polymorphisms and sepsis promises to provide clinicians with new tools to evaluate prognosis, to intervene early and aggressively in treating high-risk persons, and to avoid the use of therapies with adverse effects in treating low-risk persons.
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 | 0.248 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".