Emotion dysregulation in idiopathic rapid eye movement sleep behavior disorder
Bibliographic record
Abstract
STUDY OBJECTIVES: To characterize emotion regulation strategies in patients with idiopathic REM sleep behavior disorder (iRBD) and to explore whether these strategies are associated with clinical symptoms. METHODS: In this cross-sectional multicenter study, a total of 94 polysomnography-confirmed iRBD patients (mean age, 67.6 years; men, 56%) and 50 healthy controls (mean age, 65.4 years; men, 48%) completed the Cognitive Emotion Regulation Questionnaire (CERQ), the Korean version of the RBD questionnaire-Hong Kong (RBDQ-KR), the Buss-Durkee Hostility Inventory (BDHI), the second edition of the Beck Depression Inventory (BDI-II), and the Korean version of the Montreal Cognitive Assessment (MoCA-K). RESULTS: The iRBD group had lower CERQ adaptive scores than the control group, whereas the CERQ maladaptive scores were not significantly different between the groups. Among the CERQ adaptive subscales, the scores for positive refocusing, refocusing on planning, and positive reappraisal were significantly lower in the iRBD group than in the control group. Higher CERQ adaptive scores were correlated with lower scores on RBDQ-KR factor 1 (dream-related) and the BDI-II and higher MoCA-K scores but were not correlated with RBDQ-KR factor 2 (behavioral manifestation) or BDHI scores. Among the dream content-related items of RBDQ-KR factor 1, the CERQ adaptive score was associated only with frequent nightmares. No correlation was found between CERQ maladaptive scores and any variable except for a positive correlation with BDI-II scores. CONCLUSIONS: Our results provide evidence of emotion regulation deficits in iRBD patients. Furthermore, these results were linked to dream-related factors, especially nightmares, along with depressive symptoms and cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".