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Record W2913760863 · doi:10.5455/apd.302643976

Effects of positive psychological intervention on Parkinsons disease patients complicated with depression and cognitive dysfunction

2019· article· en· W2913760863 on OpenAlexaboutno aff
Jia Li, Chengzhi Gu, Min Zhu, Dan Li, Lan Chen, xiangyang zhu

Bibliographic record

VenueAnatolian Journal of Psychiatry · 2019
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)CognitionIntervention (counseling)Clinical psychologyPsychologyDiseaseMedicinePsychiatryPsychotherapistInternal medicineEconomics

Abstract

fetched live from OpenAlex

Objective: We aimed to evaluate the effects of positive psychological intervention on Parkinson's disease (PD) patients complicated with depression and cognitive dysfunction. Methods: Two hundred and thirty-two PD patients complicated with depression and cognitive dysfunction treated in our hospital were selected and randomly divided into two groups (n=116). The control group was treated routinely and the observation group was additionally sub-jected to positive psychological intervention for eight consecutive weeks. They were evaluated by the Hamilton Depression Rating Scale (HAMD) and the Montreal Cognitive Assessment Scale in terms of depression and cogni-tive dysfunction. The numerical data between groups were compared by the χ2 test, and the categorical data were compared by the t test. Changes in the depression and cognitive function scores before and after intervention were compared by analysis of variance for repeated measures. P

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.259
Teacher spread0.255 · 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 designNon-randomized trial
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

Citations1
Published2019
Admission routes1
Has abstractyes

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