Restless legs syndrome among blood donors: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background and Objectives Restless legs syndrome (RLS), with adverse health outcomes, has been linked to blood donation, but evidence published thus far has not been rigorously analysed. This systematic review aggregates existing evidence on RLS among blood donors and identifies associated factors worthy of further investigation. Materials and Methods MEDLINE and EMBASE were searched for articles published through 16 December 2023. Eleven studies from eight countries were selected from 142 publications. The pooled prevalence of RLS was calculated using a random‐effects model, with heterogeneity assessed by the Cochran Q and I2 statistics. Meta‐regression and sensitivity analyses explored sources of heterogeneity and the robustness of findings. Results Eleven studies, involving 20,255 blood donors, were included. The pooled prevalence of RLS among blood donors was 10.30% (95% confidence interval [CI]: 5.54%–16.30%), which was significantly higher than in the general adult population (3.0%, 95% CI: 1.4%–3.8%). Meta‐regression identified the year of study and geographical region as significant sources of heterogeneity. From the five studies that used logistic regression analyses, female sex and older age stand out as associated factors. No publication bias was detected, and sensitivity analysis confirmed the robustness of results. Conclusion Our findings suggest a high burden of RLS among blood donors, underscoring the need for further research with standardized criteria, appropriate design and analytical methodologies to better understand the impact of RLS on individual donors and the global blood supply.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".