Education Moderates the Association of Probable REM Sleep Behavior Disorder With Cognitive and Motor Impairments in Community-Dwelling Older People
Bibliographic record
Abstract
Objectives: To investigate the relationship between probable rapid eye movement (REM) sleep behavior disorder (pRBD) and cognitive/motor impairments in a community-dwelling population, and explore the moderating effects of education. Methods: In this cross-sectional study of the Beijing Longitudinal Study on Aging II (BLSA II), 4477 subjects (55 years) fulfilled the inclusion criteria. pRBD was determined by the RBD questionnaire Hong Kong (RBDQ-HK). Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to test the global cognitive performance. Walking speed was used to measure motor function. Logistic regression was performed to assess the relationship between pRBD and cognitive/motor impairments and the moderating effects of education. Results: There were 147 participants (3.3%) with pRBD. Participants with pRBD showed increased risks for cognitive impairment(OR=1.88,95%CI,1.24-2.85, p=0.003), decreased gait speed (OR=1.43, 95% CI, 1.02-2.01, p=0.03), but not for mild cognitive impairment(MCI)(measured by MoCA: OR=1.01,95%CI, 0.68-1.50, p=0.95; measured by MMSE: OR=0.90, 95%CI, 0.59-1.37, p=0.62). Education modified the effect of pRBD on MCI (measured by MoCA: p＜0.001; measured by MMSE: p =0.061) and gait speed (p=0.008). Conclusions: Our findings suggest that pRBD increases the risk of cognitive/motor impairments for a community-dwelling older population and education could alleviate the negative effects. These findings implicate that education may have beneficial effects on delaying the onset of cognitive/motor decline in pRBD subjects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".