Genetics in restless legs syndrome.
Bibliographic record
Abstract
Several studies on Restless legs syndrome (RLS) have suggested a substantial genetic contribution in the etiology of this sleep disorder. \nClinical surveys of idiopathic RLS patients have shown that up to 60% report a positive family history. Investigations of single families with \nRLS have suggested an autosomal dominant mode of inheritance with variable expressivity, and some families show possible anticipation. At \npresent, only one twin study is available, showing a high concordance rate (83.3%) between identical twins. \nDespite several reports suggesting a genetic contribution to the etiology of idiopathic RLS, few molecular genetic studies have been \ncarried out attempting to identify genes that can predispose to this disorder. In particular, genes encoding for the GABA A receptor subunits, \nthe gene for the alpha1 subunit of the glycine receptor, and genes involved in dopaminergic transmission and metabolism have been \nanalyzed, but no significant findings have been reported. \nGenome-wide studies have been conducted to map genes that play a role in vulnerability to RLS. In a single French–Canadian family \nsignificant linkage was established on chromosome 12q. The susceptibility locus on chromosome 12q was not confirmed in two South \nTyrolean families, or in our two Italian families. However, the efforts toward the identification of RLS genes must continue in order to obtain \na better characterization of the syndrome and to identify new therapeutic strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".