Motor features that distinguish isolated REM sleep behavior disorder patients from healthy controls: A systematic review
Bibliographic record
Abstract
Individuals with isolated REM sleep behavior disorder (iRBD) are at high risk of developing α-synucleinopathies, particularly Parkinson's disease (PD) and dementia with Lewy bodies (DLB). With the development of potential neuroprotective treatments for synucleinopathies, including PD, identifying clinical features that can allow for tracking subtle changes in prodromal disease and thereby monitoring risk of phenoconversion in iRBD is paramount. Subtle motor deficits have been suggested to be present in iRBD, making them potentially important clinical markers for predicting future phenoconversion. This review aims to summarize existing literature that has investigated differences in motor function between iRBD and healthy individuals, as well as progression of motor decline in iRBD. 39 eligible studies were included in this review. The results suggest that quantitative motor assessments may be more sensitive to motor impairments in this population than clinical scales. Moreover, dual-tasking tended to unmask subtle motor deficits in individuals with iRBD, particularly in gait, balance, and tapping assessments. Longitudinal studies demonstrate that motor function worsens over time in iRBD, with earliest signs of motor deficits and clear progression in tapping assessments in particular. Larger longitudinal studies that use quantitative methods of motor assessments are needed to better characterize motor progression in iRBD, and confirm the reliability of different motor markers for predicting phenoconversion of iRBD into PD and other synucleinopathies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".