Phenotypical predictors of Restless Legs Syndrome in pregnancy and their association with basal ganglia and the limbic circuits
Bibliographic record
Abstract
ABSTRACT The pregnancy-related restless legs syndrome (RLS) is thought to have a multifactorial etiology. However, the reason behind the manifestation of RLS during pregnancy remains largely elusive. A neurological and obstetrical cohort of 308 postpartum women was screened for RLS symptoms twice: 1 to 6 days (T0) and 12 weeks postpartum (T1). 57 participants were identified as affected by pregnancy-associated RLS. The clinical and anamnestic indicators of the condition were assessed by a pattern-learning classifier trained to predict the RLS status. Structural MRI was obtained from 25 of the 57 participants with RLS history in pregnancy. In this sample, a multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors. The RLS prevalence rate in our sample was 19% (n=57), with the women suffering from RLS being older, more often unmarried, affected by gestational diabetes and having been more exposed to stressful life events. A history of RLS and the severity and frequency of repetitive compulsive movements were found to be the strongest predictors of RLS manifestation. In the RLS group, high cortisol levels, being married and receiving iron supplements were found to be associated with increased volumes in the bilateral striatum. Investigating pregnancy-related RLS in a frame of brain phenotype modes may help shed light on the heterogeneity of the condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".