Characteristics and correlations of sleep disorders in patients with relapsing–remitting multiple sclerosis in China: a cross-sectional study
Bibliographic record
Abstract
Background Sleep disorders are a major but overlooked symptom in patients with multiple sclerosis (MS). Objectives This article aims to investigate the characteristics of sleep disorders in patients with relapsing–remitting multiple sclerosis (RRMS) and to analyze the correlations between sleep disorders in RRMS and anxiety, depression, fatigue, and cognitive impairment. Methods A total of 35 patients with RRMS and 35 controls were included, and both groups underwent assessments for sleep, anxiety, depression, fatigue, and cognitive function. Results The RRMS group and the control group showed significant differences in Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Modified Fatigue Impact Scale (MFIS), and Montreal Cognitive Assessment (MoCA). The group with poor sleep quality (PSQI > 5) had significantly higher scores on the AIS, ISI, HAMA, and HAMD Scale compared to the group with good sleep quality ( p = 0.036, p < 0.001, p = 0.036, p = 0.054). The PSQI showed a negative correlation with disease duration; the PSQI showed a positive correlation with HAMA, HAMD, and Activities of Daily Living (ADL); AIS, ISI, and Sleep Hygiene Awareness and Practice Scale (SHAPS) all demonstrated significant positive correlations with MFIS, HAMA, and HAMD; Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS) showed a negative correlation with HAMA and HAMD. Conclusion Sleep disorders, fatigue, anxiety, depression, and cognitive impairments are more likely to occur in patients with RRMS; there is a certain correlation between PSQI, AIS, ISI, SHAPS, and DBAS scores in the RRMS group and fatigue, anxiety, and depression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".