Free water predicts dementia with Lewy bodies in isolated REM sleep behavior disorder
Bibliographic record
Abstract
INTRODUCTION: Most individuals with isolated rapid eye movement sleep behavior disorder (iRBD) develop dementia with Lewy bodies (DLB) or Parkinson's disease (PD). Brain biomarkers predicting specific phenoconversion trajectories are lacking. METHODS: In this multicenter diffusion magnetic resonance imaging study (261 iRBD, 177 controls), free water (FW) was measured in the nucleus basalis of Meynert (NBM) and posterior substantia nigra (SN). Among 230 iRBD patients with follow-up, 64 converted (16 DLB, 38 PD). Time-to-event analyses were performed to assess differential phenoconversion. RESULTS: Phenoconverters had higher FW in the NBM and posterior SN. Only FW in the NBM predicted conversion to DLB over PD. NBM volume predicted DLB conversion, but only FW remained significant when both were modeled. FW in the NBM correlated with lower MoCA scores in iRBD. DISCUSSION: FW in the NBM is a sensitive biomarker of cognitive decline and DLB progression in iRBD, outperforming volume and supporting its use in early stratification. HIGHLIGHTS: FW in the NBM specifically identifies conversion to DLB. Increased FW in the NBM is associated with lower global cognition in iRBD. FW in the SN in iRBD does not relate more to DLB than PD. FW in the NBM is a biomarker of differential phenoconversion in iRBD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".