Prevalence of restless legs syndrome in people with diabetes mellitus: A pooling analysis of observational studies
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) is associated with different clinical complications. The aim of this study was to explore the prevalence of RLS in people with diabetes mellitus and compare the risk of restless leg syndrome (RLS) between diabetic and non-diabetic population. Methods: We searched for studies of RLS prevalence in DM through PubMed, Embase, and Web of Science. Two authors independently completed the literature screening, data extraction, and bias risk assessment of eligible studies. All observational studies that assessed the prevalence or risk of RLS in DM were included, where the diagnosis of RLS was based on the International Restless Legs Syndrome Study Group (IRLSSG). Percentages, odds ratio (OR) with 95% confidence intervals (CI) were used to assess pooled estimates of RLS prevalence and risk based on random-effects models. Newcastle-Ottawa-scale (NOS) or a modified NOS were used to evaluate the quality of studies. Findings: >75%), all of observational design. Interpretation: Our study suggests that the prevalence and risk of RLS might be higher in DM patients than in non-diabetes population. However, given limitations in the analysis and study design, the findings need to be corroborated in future studies. Funding: This work was supported by the Basic Conditions Platform Construction Project of Sichuan Science and Technology Department (2019JDPT0015), and the "1・3・5 project for disciplines of excellence, West China Hospital, Sichuan University" (ZYJC18003).
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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 | 0.043 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".