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血管源性帕金森综合征抑郁、焦虑与其他症状的关系研究 Correlation Between Depression, Anxiety and Other Symptoms in Vascular Parkinsonism Patients

2018· article· zh· W4376297709 on OpenAlexaboutno aff
张晓颖 马惠姿

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languagezh
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)AnxietyParkinsonismCorrelationClinical psychologyMedicinePsychiatryPsychologyInternal medicineDiseaseMathematics

Abstract

fetched live from OpenAlex

目的 研究血管源性帕金森综合征(vascular parkinsonism,VP)患者焦虑、抑郁的发病率以及焦虑、 抑郁与运动症状、非运动症状的关系。 方法 连续入选VP患者,根据汉密尔顿焦虑量表(Hamilton a nxiety s cale,HAMA)和汉密尔顿抑郁量 表(Hamilton depression scale,HAMD)评定结果将患者分为情绪障碍组和无情绪障碍组。比较两组的 运动功能[运动障碍学会帕金森病综合评量表(Movement Disorder Society-Sponsored Revision Unified Parkinson’s Disease Rating Scale,MDS-UPDRS)第三部分],认知功能[简易智能精神状态检查量表 (mini-mental state examination,MMSE)、蒙特利尔认知评估量表(Montreal cognitive assessment,MoCA) 评分],睡眠情况[爱泼沃斯思睡量表(Epworth sleeping scale,ESS)、匹兹堡睡眠质量指数(Pittsburgh sleep quality index,PSQI)评分]。根据Hoehn-Yahr分期将患者分为轻、中、重度VP,比较3组情绪障碍的发 病率。另外,对患者的HAMA和HAMD分值与上述运动功能和其他非运动症状评分进行相关性分析。 结果 共纳入60例VP患者,伴发情绪障碍组46例(76.67%),无情绪障碍组14例(23.33%)。情绪 障碍组较无情绪障碍组Hoehn-Yahr分期[2.0(2.0,3.0)vs 2.0(2.0,2.2),P =0.04]、UPDRS-Ⅲ评分 ([ 51.91±8.67)分 vs(39.72±7.84)分,P=0.02]、PSQI评分([ 14.77±4.56)分 vs(9.28±5.33)分, P =0.04]更高,差异有统计学意义。按照Hoehn-Yahr分期分为轻度32例,中度18例,重度10例,其中,中度 组和重度组情绪障碍发生率高于轻度组,差异有统计学意义。另外,VP患者HAMA评分及HAMD评分与 MDS-UPDRS Ⅲ评分、ESS评分及PSQI评分正相关。 结论 情绪障碍在VP患者中的发生率较高,其中重度VP患者情绪障碍发生率更高。有情绪障碍的VP 患者运动症状和非运动症状均更显著。 Abstract: Objective To investigate the prevalence rates of mood disorders (MD) including anxiety and depression in vascular parkinsonism (VP) patients and their relationships with motor symptoms and other non-motor symptoms. Methods VP patients were consecutively enrolled and divided into MD group and non-MD group based on the scores of Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD). The motor function was compared between the two groups based on the Movement Disorder Society-Sponsored Revision Unified Parkinson’s Disease Rating Scale section III (MDS-UPDRS III), and the non-motor functions including cognition and sleeping were compared according to the results of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Epworth Sleeping Scale (ESS), and Pittsburgh Sleep Quality Index (PSQI). Patients were divided into mild, moderate, and severe VP according to Hoehn-Yahr staging, and the prevalence rates of MD in these three groups were compared. Finally, the potential correlations of the HAMA and HAMD scores with the above motor function scores and other non-motor function scores were analyzed. Results A total of 60 cases of VP were enrolled. Of all, there were 46 patients (76.67%) in MD group and 14 (23.33%) in non-MD group. The scores of Hoehn-Yahr staging [2.0 (2.0, 3.0) vs 2.0 (2.0, 2.2), P =0.04], UPDRS-III [(51.91±8.67) vs (39.72±7.84), P =0.02], PSQI [(14.77±4.56) vs (9.28±5.33), P =0.04] scores were significantly higher in MD group than in non-MD group. According to Hoehn- Yahr staging, there were 32, 18 and 10 patients in mild, moderate, and severe VP groups respectively, and the prevalence rates of MD were significantly higher in moderate and severe VP group than in mild VP group. The HAMA score and HAMD score of VP patients were positively correlated with the MDS-UPDRS III score, ESS score, and PSQI score. Conclusion The prevalence of MD in VP patients is relatively high, especially in patients with severe VP. Both motor symptoms and non-motor symptoms are more severe in VP patients with MD.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.475
Teacher spread0.383 · 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".

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Published2018
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