MétaCan
Menu
Back to cohort
Record W2895665974

The Benefits of Temu Mangga (Curcuma Mangga Val) in Cognitive Functions of Elderly

2018· article· en· W2895665974 on OpenAlexaboutno aff
N. Saelan Tadjudin, A. Jayalangkara, Andi Asadul Islam, Suryani As’ád, Muhammad Nasrum Massi, Ilhamjaya patellongif

Bibliographic record

VenueInternational Journal of Sciences: Basic and Applied Research · 2018
Typearticle
Languageen
FieldMedicine
TopicMedicinal Plants and Neuroprotection
Canadian institutionsnot available
Fundersnot available
KeywordsMann–Whitney U testMontreal Cognitive AssessmentMedicineWilcoxon signed-rank testRandomized controlled trialSignificant differenceInternal medicineCognitionCognitive impairmentPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the benefit of Temu Mangga extract (Curcuma Mangga Val) with cognitive function test using Montreal Cognitive Assesment- Indonesian version (MoCA-Ina -Registry MoCA-Ina / stroke registry-INA 2012) in Elderly. This Clinical trial study was conducted using a double-blind, randomized controlled trial, pre and post-test group design in 85 female participants from Panti Wreda Nursing Home in Jakarta. A total of 83 participants who followed the study to the end. 43 participants were administered the Temu Mangga Capsule (TM) group 3x500 mg/day for 30 days.  Results: Mean score of MoCA-Ina in TM group increased by 2.37 from 23.93 ± 3.73 to 26.30 ± 3.92 with Wilcoxon test p-value = 0.000, and in the control group with 40 participants increased by 2.55 from 23.58 ± 4.60 to 26.13 ± 4.56 with Wilcoxon test p-value = 0.000. Mann-Whitney test results in both groups p > 0.05. Conclusions: There was an increase in each treatment groups and control groups with significant Moca-Ina values, but between the two groups did not show significant difference changes. Psychological factors such as attention and hope will become healthier are a strong factor in this study.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.121
GPT teacher head0.402
Teacher spread0.281 · 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 designOther design
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sciences: Basic and Applied ResearchSame topicMedicinal Plants and NeuroprotectionFrench-language works237,207