Characterization of memory profile in idiopathic REM sleep behavior disorder
Bibliographic record
Abstract
OBJECTIVE: The present study aims to examine whether declarative memory dysfunction relates to impaired core memory mechanisms or attentional and executive dysfunction in idiopathic REM Sleep Behavior Disorder (iRBD). METHOD: In this observational, cross-sectional study, were enrolled 82 individuals with the diagnosis of iRBD according to the International Classification of Sleep Disorders and 49-matched healthy controls fulfilling inclusion criteria. All participants underwent two memory tasks, namely the Rey Auditory Verbal Learning Test (RAVLT) and Memory Binding Test (MBT), which include conditions of varying degrees of dependence on executive functioning, as well as different indicators of core memory processes (e.g., learning, retention, relational binding). RESULTS: = -0.37, 95% PPI [-0.69, -0.05]), but not on delayed recognition of the same material. Their performance in cued recall tasks both in immediate and delayed conditions was in comparison to controls relatively spared. Moreover, the deficit in delayed free recall was mediated by attention/processing speed. CONCLUSIONS: In iRBD, we replicated findings of reduced free recall based on inefficient retrieval (retrieval deficit), which was small in terms of effect size. Importantly, the memory profile across measures does not support the presence of core memory dysfunction, such as poor learning, retention or associative binding.
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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) | 0.001 | 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".