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Record W3170564000 · doi:10.3889/oamjms.2021.6364

The Correlation between the Indonesian Version of Montreal Cognitive Assessment and Homocysteine Levels in Bataknese Male with Schizophrenia in Prof. DR. M. Ildrem Psychiatric Hospital Medan

2021· article· en· W3170564000 on OpenAlexaboutno aff
Yoseva Hotnauli, Bahagia Loebis, Muhammad Surya Husada, Nazli Mahdinasari Nasution, Elmeida Effendy

Bibliographic record

VenueOpen Access Macedonian Journal of Medical Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHomocysteineMontreal Cognitive AssessmentMedicineSchizophrenia (object-oriented programming)IndonesianCognitionInternal medicineCorrelationPsychiatryPathogenesisCross-sectional studyCognitive impairmentPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The pathogenesis of schizophrenia and its mechanism is not convinced. Several studies indicate that schizophrenia pathogenesis can be related to changes at the cellular level. The studies show homocysteine in people with schizophrenia was significantly increased, abnormal homocysteine metabolism can lead to DNA methylation. AIM: This study aimed to establish the correlation between the Indonesian Montreal Cognitive Assessment Version (MoCA-Ina) scores and homocysteine levels in males with Bataknese schizophrenia. METHODS: This study is a numerical correlative analytic study with an approach to cross-sectional study; by evaluating the correlation between the MoCA-Ina scores and the level of homocysteine in males with Bataknese schizophrenia. RESULTS: The median of the MoCA-Ina score in the study subjects was 22, with a minimum score of 18 and a maximum score of 26. There was a significant correlation between the MoCA-Ina scores and Homocysteine levels (p = 0.001). CONCLUSION: The interpretation obtained from this study is that the higher Homocysteine level, the lower the MoCA-Ina scores in Bataknese men with schizophrenia is.

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)0.003
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 score0.089
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.397
Teacher spread0.353 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOpen Access Macedonian Journal of Medical SciencesSame topicChild Nutrition and Feeding IssuesFrench-language works237,207