<scp>REM</scp> sleep behaviour disorder is associated with lower fast and higher slow sleep spindle densities
Bibliographic record
Abstract
To investigate differences in sleep spindle properties and scalp topography between patients with rapid eye movement sleep behaviour disorder (RBD) and healthy controls, whole-night polysomnograms of 35 patients diagnosed with RBD and 35 healthy control subjects matched for age and sex were compared. Recordings included a 19-lead 10-20 electroencephalogram montage and standard electromyogram, electrooculogram, electrocardiogram and respiratory leads. Sleep spindles were automatically detected using a standard algorithm, and their characteristics (amplitude, duration, density, frequency and frequency slope) compared between groups. Topological analyses of group-discriminative features were conducted. Sleep spindles occurred at a significantly (e.g. t34 = -4.49; P = 0.00008 for C3) lower density (spindles ∙ min(-1) ) for RBD (mean ± SD: 1.61 ± 0.56 for C3) than for control (2.19 ± 0.61 for C3) participants. However, when distinguishing slow and fast spindles using thresholds individually adapted to the electroencephalogram spectrum of each participant, densities smaller (31-96%) for fast but larger (20-120%) for slow spindles were observed in RBD in all derivations. Maximal differences were in more posterior regions for slow spindles, but over the entire scalp for fast spindles. Results suggest that the density of sleep spindles is altered in patients with RBD and should therefore be investigated as a potential marker of future neurodegeneration in these patients.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".