Screening questions for the diagnosis of restless legs syndrome in hemodialysis
Bibliographic record
Abstract
BACKGROUND: Restless legs syndrome (RLS) is common in end-stage renal disease and is associated with reduced health-related quality of life. Simple and accurate screening instruments are needed since RLS is underdiagnosed and treatable. We examined the operating characteristics of screening questions and a disease-specific measurement tool for the diagnosis of RLS in hemodialysis. METHODS: We conducted a cohort study of prevalent adult hemodialysis patients in Hamilton, Canada. The diagnosis of RLS was made using the 2012 Revised International Restless Legs Syndrome Study Group (IRLSSG) criteria. All participants received three screening instruments: (i) a single screening question for RLS derived from a nondialysis population; (ii) a single question from the Edmonton Symptom Assessment System (ESAS); and (iii) the IRLSSG Rating Scale (IRLS). All instruments were compared with the reference standard using logistic regression from which receiver operating characteristics curves were generated. Cutoffs associated with maximum performance were identified. RESULTS: We recruited 50 participants with a mean (SD) age of 64 (12.4) years, of whom 52% were male and 92% were on three times weekly hemodialysis. Using the reference standard, 14 (28%) had a diagnosis of RLS. The single screening question for RLS had an area under the receiver operating curve (AUROC) of 0.72 with a sensitivity of 85.7% and specificity of 58.3%. An ESAS cutoff of ≥1 had the highest AUROC at 0.65 with a sensitivity of 79% and specificity of 56%. An IRLS cutoff of ≥20 had the highest AUROC at 0.75 with a sensitivity of 71% and specificity of 81%. CONCLUSION: IRLS had better specificity than the single question or ESAS for the diagnosis of RLS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".