Visuospatial dysfunction predicts dementia-first phenoconversion in isolated REM sleep behaviour disorder
Bibliographic record
Abstract
OBJECTIVE: While isolated rapid eye movement sleep behaviour disorder (iRBD) is known as a prodrome of α-synucleinopathies, the prediction for its future phenoconversion to parkinsonism-first or dementia-first subtype remains a challenge. This study aimed to investigate whether visuospatial dysfunction predicts dementia-first phenoconversion in iRBD. METHODS: Patients with iRBD and control subjects were enrolled in this prospective cohort study. Baseline neuropsychological assessment included the Unified Parkinson's Disease Rating Scale part III, Montreal Cognitive Assessment (MoCA), Rey-Osterrieth complex figure (ROCF), Colour Trails test (CTT), Farnsworth-Munsell 100-hue test and Digit Span test. The anterior and posterior subscores of MoCA as well as their modified versions were explored. A composite score derived from ROCF and CTT was also explored. Regular follow-up was conducted to determine the phenoconversion status of iRBD patients. RESULTS: The study included 175 iRBD patients and 98 controls. During a mean follow-up of 5.1 years, 25.7% of patients experienced phenoconversion. Most of the neuropsychological tests could differentiate dementia-first but not parkinsonism-first convertors from non-convertors. The modified posterior subscore of MoCA, by integrating the Alternating Trail Making and Clock Drawing components into original the posterior subscore, which mainly reflects visuospatial function, was the strongest predictor for dementia-first phenoconversion (adjusted HR 5.48, 95% CI 1.67 to 17.98). CONCLUSION: Visuospatial dysfunction, as reflected mainly by the modified posterior subscore of MoCA, is a predictive factor for dementia-first phenoconversion in iRBD, suggesting its potential for being a biomarker for clinical prognostic prediction and potential neuroprotective trials aiming to delay or prevent dementia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".