Suggested immobilization test for diagnosis of restless legs syndrome in Parkinson's disease
Bibliographic record
Abstract
Diagnosis of restless leg syndrome (RLS) in Parkinson's disease (PD) is difficult because of clinical confounds. The suggested immobilization test (SIT) is validated for diagnosis of primary RLS. This study evaluated the usefulness of the SIT for diagnosis of RLS in PD. We compared SIT scores, as well as polysomnography measures in 50 patients with PD (25 with RLS, 25 without), 25 patients with primary RLS, and 25 age/sex matched controls. Mean leg discomfort score was increased in patients with PD and RLS compared to PD without RLS, and also in patients with primary RLS compared to controls. Leg discomfort was significantly higher at the end of the test in patients with RLS compared to patients without RLS. Intensity of leg discomfort was similar between patients with RLS, with or without PD. Using a mean leg discomfort cutoff of 11, we showed sensitivity of 91% and specificity of 72% for RLS diagnosis in PD during symptomatic time intervals. Periodic leg movements index during the SIT did not differ between groups. Periodic leg movements index during sleep and wakefulness was increased in patients with primary RLS compared to controls, but did not differ between patients with PD, with and without RLS. The sensory SIT is a simple test that may help diagnose RLS in patients with PD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".