Excessive daytime sleepiness in Chinese patients with sporadic amyotrophic lateral sclerosis and its association with cognitive and behavioural impairments
Bibliographic record
Abstract
OBJECTIVE: To examine the frequency and clinical features of excessive daytime sleepiness (EDS) and its association with cognitive and behavioural impairments in patients with amyotrophic lateral sclerosis (ALS). METHODS: We conducted a cross-sectional investigation to explore the frequency and clinical features of EDS in a group of 121 Chinese patients with ALS compared with 121 age-matched and sex-matched healthy subjects. EDS was diagnosed using the Epworth Sleepiness Scale (ESS). Other characteristics of patients with ALS including sleep quality, REM sleep behaviour disorder (RBD), restless legs syndrome (RLS), cognition, behaviour, depression and anxiety were also evaluated. RESULTS: EDS was significantly more frequent in patients with ALS than in controls (26.4% vs 8.3%; p<0.05). Patients with ALS with EDS scored lower scores on the revised ALS Functional Rating Scale (ALSFRS-R), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and MMSE and MoCA delayed memory subitems and higher on the Frontal Behavioural Inventory (FBI) than patients with ALS without EDS. ESS scores correlated with global ALSFRS-R, FBI, MMSE and MoCA scores and MMSE and MoCA delayed memory scores. RLS and global ALSFRS-R scores were independently associated with EDS in patients with ALS. CONCLUSIONS: We identified a high frequency of EDS symptoms in Chinese patients with ALS, and these patients might have more serious physical, cognitive and frontal behaviour impairment. Patients with ALS might improve quality of life from the timely recognition and optimised management of EDS symptoms. Our results further suggest that ALS is a heterogeneous disease that might exhibit abnormal sleep-wake patterns.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".