[An analysis of clinical characteristics and factors in Parkinson's disease patients with excessive daytime sleepiness].
Bibliographic record
Abstract
OBJECTIVE: To explore the clinical characteristics and prognostic factors in patients with Parkinson's disease (PD) and excessive daytime sleepiness (EDS) and to identify whether EDS could influence the sleep architecture and sleep apnea-related parameters. METHODS: A total of 130 PD patients were eligible and enrolled in the study.By the Epworth sleepiness scale (ESS), patients were divided into the EDS group (ESS≥10) with 61 patients and the non-EDS group (ESS<10) with 69 patients.All underwent a video-polysomnography (PSG). Clinical characteristics were mainly evaluated by the unified Parkinson's disease rating scale (UPDRS) and the Hoehn-Yahr(H-Y)stage.while other related scales were applied for evaluating depression, cognitive function, quality of sleep and quality of life. RESULTS: A total of 61 patients (46.92%) were diagnosed as EDS (ESS≥10). Compared to the non-EDS group, the EDS group had significantly higher score of HAMD, UPDRSⅠand UPDRSⅡ, and significantly lower score of MoCA and PDQ (all P<0.05). Non-conditional logistic regression analysis showed that the scores of HAMD and UPDRSⅠ were the main prognostic factors for EDS.Significantly decreased sleep latency (SL) was found in the EDS group by PSG (P=0.008). The score of ESS was showed to be correlated with the scores of HAMD, MoCA, UPDRSⅠ, UPDRSⅡ, PDQ and SL. CONCLUSIONS: PD patients with EDS have more severe depression and cognitive dysfunction and worse quality of life.Sleep structure is altered in those patients with decreased sleep latency.Mental status is closely associated with EDS, but not sleep apnea.
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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 | 0.000 | 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) | 0.002 | 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".