Use of Mendelian randomization to better understand and treat sepsis
Bibliographic record
Abstract
Discovering novel therapies and defining the causal contribution of genes, proteins and lipids is challenging in sepsis, because sepsis is so heterogeneous; effective therapies in one patient may not be effective in another, explaining in part why sepsis trials have not been very successful.We highlight Mendelian randomization (MR), a statistical methods that first helps establish causal relationships between intermediate phenotypes such as plasma proteins and clinical phenotypes using genetics.In sepsis, there are innumerable studies showing significant associations between proteins, metabolites, other biomarkers and outcomes, yet the causal contribution of biomarkers is highly uncertain, potentially due to confounding, reverse causation, or because, for example, inflammatory markers are non-specific epiphenomena.Genetics are a powerful tool to interrogate which intermediate traits, including plasma proteins, metabolites, or even radiographic features, contribute to a disease.MR also enhances prognostic and predictive enrichment of sepsis trials by better defining biologically relevant clinical subgroups.The technique has been successfully used to define several traits with causal contributions to risk for acute respiratory distress syndrome (ARDS) [1-3] and to sepsis mortality [4, 5], highlighting pathways which warrant targeting and identifying specific at-risk populations.Thus, MR holds promise to advance successful precision sepsis trials.
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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 | 0.087 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".