The Potential to Inform Statin Use in Multiple Sclerosis Through Human Genetics
Bibliographic record
Abstract
Less than 10% of drugs that enter phase 1 clinical development ultimately receive regulatory approval, often due to shortcomings in safety or efficacy. The recognition that drugs with genetically supported targets have a two-fold higher approval rate has led to the incorporation of human genetic approaches in drug development.1 One such approach is Mendelian randomization (MR), which uses genetic variants to enable causal inference between exposures and outcomes. These exposures can be complex traits, such as body mass index, or in the context of drug discovery, levels of genes, or proteins. For instance, genetic variants in the 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) gene region that predispose individuals to higher or lower levels of low-density lipoprotein (LDL) cholesterol can predict the effect of HMGCR inhibitors (statins) on cardiovascular disease.2 This genetically mimics statin exposure and can provide insight into the efficacy, adverse effects, and repurposing potential of these and other therapeutics. Because allocation to genetic variation in levels of the exposure (e.g., LDL cholesterol) is random and set at conception, MR reduces the risk of confounding and reverse causality and provided key assumptions are met, can mirror randomized clinical trials at a much lower cost.3
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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".