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Record W2790108106 · doi:10.26891/jik.v11i1.2017.12-18

Hubungan Gangguan Fungsi Kognitif dengan Hipertensi Menggunakan Montreal Cognitive Assessment Versi Indonesia (Moca-Ina)

2018· article· en· W2790108106 on OpenAlexaboutno aff
Enny Lestari, Melfi Riqqah, Ilhami Romus

Bibliographic record

VenueJurnal Ilmu Kedokteran (Journal of Medical Science) · 2018
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentGerontologyNursing homesCognitive impairmentCognitionElderly peopleMedicinePsychologyNursingPsychiatry

Abstract

fetched live from OpenAlex

High blood pressure associated with a reduction of cognitive function in young adults and older age. One of cognitiveimpairment examination is Montreal Cognitive Assessment (MoCA). The aim of this study is to determine the correlationbetween impaired cognitive function with hypertension by using MoCA–INA in the elderly people at Tresna WerdhaKhusnul Khotimah nursing home Pekanbaru. This research obtained the incidence of impaired cognitive function inelderly people at Tresna Werdha Khusnul Khotimah nursing home Pekanbaru as many as 28 subjects (90.3%).Hypertension in the elderly at Tresna Werdha Khusnul Khotimah nursing home Pekanbaru were 23 subjects consistof 6 subjects (19.35%) with controlled hypertension and 17 subjects (54.84%) with uncontrolled hypertension. Theresults of statistical test by using Fisher test obtained p value as big as 1.000 (p>0.05). This shows there is nosignificant correlation between impaired cognitive function with hypertension in the elderly people at Tresna WerdhaKhusnul Khotimah nursing home Pekanbaru.

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)0.002
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.364
Teacher spread0.337 · 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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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