Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is a complex trait and highlights the interplay between nature, nurture and the stochastic events of development. Genetic‐epidemiological studies have shown the importance of the genetic contribution to the marked familial clustering of MS. Environmental factors also increase MS risk and act at a population‐based level. Climate and the subsequent effect on vitamin D levels are thought to play an important role. There is a clear association with MS and human leukocyte antigen (HLA) polymorphisms located at chromosome 6p21. Individuals who carry the at‐risk genotypes (DRB1 * 15/15) have a five‐ to sixfold increased chance of MS. Genome‐wide searches for other non‐HLA susceptibility genes by association have been successful in identifying many disease genes of mild effects. The genes implicated have an immune‐related function. There are likely additional genes contributing to MS risk and new technologies may prove fruitful for their identification. Key Concepts: Genetic‐epidemiological studies have shown the importance of genes to MS risk and studies such as twin, adoptive and half‐siblings are useful tools in the study of complex traits. The search for genes in complex traits is difficult and often requires hundreds to thousands of cases and unaffected controls. This is due to the small effect of any given risk gene. A risk‐related gene may be modified by its interaction with other genes and the environment and may be further modified by chance developmental processes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.093 | 0.022 |
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".