Genetic risk factors for stroke in the genome-wide association era
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Recent genome-wide association studies (GWASs) have renewed interest in genetic determinants of a wide range of complex traits and disorders, including stroke. AREAS COVERED IN THIS REVIEW: This paper reviews the current knowledge of genes that contribute to rare monogenic forms of stroke as well as more common 'garden variety' forms, focusing on the results of GWASs. Potential clinical pharmacogenetic and diagnostic applications of this information are considered. Publications from 1990 to September 2010 were identified through a Medline search using terms 'human stroke' and 'genetics', 'monogenic', 'familial', 'mutation', 'genome-wide association study', 'polymorphism', or 'genotype'. WHAT THE READER WILL GAIN: The review synthesizes and collates the current understanding of genes that are involved across a range of stroke subphenotypes. TAKE HOME MESSAGE: The complexity of stroke will make translation of genetic findings into new diagnostic or therapeutic tools relatively more challenging than for some other conditions and tempers the authors' enthusiasm for the eventual clinical utility of this information.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".