MétaCan
Menu
← Back to cohort
Record W2363749020

The study of subjective and objective evaluation of sleep disturbances in Parkinson's disease

2013· article· en· W2363749020 on OpenAlexaboutno aff
Yan Gong, Hong Yu, Chengjie Mao, Weidong Hu, Kangping Xiong, Yun Shen, Chun‐Feng Liu

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsEpworth Sleepiness ScalePittsburgh Sleep Quality IndexMontreal Cognitive AssessmentMedicineParkinson's diseaseRating scalePolysomnographyPhysical therapyHamdLevodopaInternal medicineDepression (economics)Sleep disorderDiseaseInsomniaPsychologyPsychiatrySleep qualityCognitive impairmentApneaSignificant difference
DOInot available

Abstract

fetched live from OpenAlex

Background Sleep disorder is one of the most common non-motor symptoms in Parkinson's disease (PD) patients. At present, there are subjective and objective tools to evaluate sleep disorders. Nevertheless, previous studies commonly used single subjective questionnaires or objective examinations. Therefore, we used the combinations of subjective and objective tools to analyze clinical characteristics of sleep disturbances in PD and investigated differences and consistence between subjective and objective tools. Methods One hundred and sixteen PD patients were eligible to participate into this study. All participants were evaluated by Pittsburgh Sleep Quality Index (PSQI), Unified Parkinson's Disease Rating Scale (UPDRS) in "on" condition, Hoehn-Yahr (H-Y) stage, Hamilton Depression Rating Scale (HAMD 24 items), Montreal Cognitive Assessment (MoCA), Epworth Sleepiness Scale (ESS), and underwent a video-polysomnography (Video-PSG). Results According to PSQI score of 116 PD patients, the proportion of PD patients with sleep disturbances (PSQI ≥ 7) was 50% (N = 58). Compared to PD patients without sleep disturbances, PD patients with sleep disturbances had lower score of MoCA (23.34 ± 3.50 vs 24.89 ± 3.52; t = 2.377, P = 0.019), higher score of UPDRSⅠ[4.00 (2.00, 5.00) vs 3.00 (2.00, 5.00); U = - 2.306, P = 0.021], UPDRSⅡ[12.00 (9.00, 16.00) vs 10.00 (6.00, 13.00); U = - 1.995, P = 0.046], higher levodopa equivalent daily dose [LED, (508.14 ± 335.85) vs (394.06 ± 236.40) mg/d; t = - 2.115, P = 0.037]. Although PD patients with sleep disturbances had more score of UPDSR Ⅲ and higher H-Y stage, the differences were not significant (P > 0.05). On the other hand, decreased total sleep time (TST), reduced sleep efficiency (SE), increased sleep latency (SL), decreased non-rapid eye movement (NREM) sleep stage Ⅱ time were found for PD patients with sleep disturbances (P < 0.05, for all). Other PSG parameters had no significant differences between PD patients with and without sleep disturbances (P > 0.05, for all). The score of PSQI was positively correlated with the score of ESS (r = 0.200, P = 0.032), HAMD (r = 0.202, P = 0.030), UPDRSⅠ (rs = 0.266, P = 0.004) and Ⅱ (rs = 0.254, P = 0.007), LED (r = 0.213, P = 0.022), SL (rs = 0.211, P = 0.023). Moreover, the score of PSQI was negatively correlated with TST (r = -0.231, P = 0.003), SE (r = -0.192, P = 0.039) and MoCA (r = -0.236, P = 0.011). Conclusion PD patients with sleep disturbances had worse cognition impairment, more mood disorders, decreased activity of daily life. Meanwhile, most of PSG parameters were altered in PD patients with sleep disturbances. Moreover, the severity of sleep disturbances in PD patients was correlated with these factors. Overall sleep quality of PD patients assessed with the objective tool could be predicted by the subjective scale. However, to evaluate sleep architecture and other sleep disorders for PD patients, the objective tools (such as Video-PSG monitoring) are necessary to be used.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.159
GPT teacher head0.508
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicParkinson's Disease Mechanisms and Treatments→French-language works237,207→