MétaCan
Menu
Back to cohort

Effects of butylphthalide soft capsules on cognitive function and daily living ability in patients with Parkinson's disease dementia

2019· article· en· W3029069535 on OpenAlexaboutno aff
Xiaowei Ying, Lingxiao Li, Yongqiang Zhang

Bibliographic record

VenueZhongguo jiceng yiyao · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineActivities of daily livingDementiaCognitionMini–Mental State ExaminationInternal medicineDiseasePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Objective To investigate the effects of butylphthalide soft capsules on cognitive function and daily living ability in patients with Parkinson's disease dementia(PDD). Methods From January 2016 to January 2017, 90 patients with PDD in the First People's Hospital of Wenling were divided into control group and study group by random number table method, with 45 cases in each group.The patients in the two groups were treated with conventional symptomatic treatment, and the control group was treated with donepezil, the study group was treated with butylphthalide soft capsules on this basis.The treatment time was 12 weeks.The clinical efficacies of the two groups were compared.Before treatment and 12 weeks after treatment, the cognitive functions of patients were assessed by Montreal cognitive assessment scale (MoCA) and mini-mental state examination (MMSE), and the daily living abilities of patients were assessed by Barthel index scale. Results The total effective rate of the study group was 93.33%, which was higher than 75.56% of the control group (χ2=5.414, P 0.05). Conclusion Butylphthalide soft capsules can significantly improve the cognitive function and daily living ability of patients with PDD, which is suitable for clinical application and promotion. Key words: Parkinson disease; Dementia; Cognitive; Activitise of daily living; Butylphthalide soft capsules

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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 teacher head, 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueZhongguo jiceng yiyaoSame topicNeurological Disease Mechanisms and TreatmentsFrench-language works237,207