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Record W7029264693

HUBUNGAN ANTARA KADAR high sensitivity-C REACTIVE PROTEIN (hs-CRP) TINGGI DAN GANGGUAN FUNGSI KOGNITIF YANG DI UKUR DENGAN MoCA INA PADA PASIEN LANSIA DENGAN DIABETES MELLITUS TIPE 2 DI RSUD DR.SOETOMO SURABAYA

2018· dissertation· id· W7029264693 on OpenAlexaboutno aff

Bibliographic record

VenueUniversitas Airlangga Repository (Universitas Airlangga) · 2018
Typedissertation
Languageid
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetes mellitusBody weightSerum glucose
DOInot available

Abstract

fetched live from OpenAlex

Latar Belakang dan Tujuan
\nC-reactive protein merupakan penanda proses inflamasi dan penyakit vaskuler.
\nKadar hs-CRP tinggi berhubungan dengan kerusakan jalur frontal-subkortikal
\nsehingga mempengaruhi fungsi kognitif. Tujuan penelitian ini adalah untuk
\nmengetahui hubungan antara kadar hs-CRP tinggi dan gangguan fungsi kognitif
\nyang di ukur dengan MoCA-INA pada pasien lansia dengan Diabetes Mellitus
\nTipe 2 di RSUD Dr. Soetomo Surabaya.
\nMetode
\nStudi kasus kontrol dilakukan pada 90 pasien lansia dengan DM tipe 2 yang
\ndatang ke Poliklinik Endokrinologi dan Poliklinik Geriatri RSUD Dr Soetomo
\nsejak Desember 2017 hingga Januari 2018. Subyek penelitian dievaluasi fungsi
\nkognitifnya dengan pemeriksaan Montreal Cognitive Assessement Versi Indonesia
\n(MoCA-INA) kemudian dibagi menjadi kelompok kasus dan kelompok kontrol.
\nSetelah itu subyek dilakukan pengambilan darah lengkap dan kadar hs-CRP.
\nAnalisis data menggunakan uji chi square.
\nHasil
\nDidapatkan 90 subyek penelitian yang terbagi menjadi 45 subyek dalam
\nkelompok kasus (29 perempuan, 16 laki-laki) dan 45 subyek dalam kelompok
\nkontrol (26 perempuan, 19 laki-laki). Kadar hs-CRP tinggi terdapat pada 30
\n(66,7%) orang pada kelompok kasus dan 28 (62,2%) orang pada kelompok
\nkontrol. Tidak terdapat hubungan yang bermakna antara kadar hs-CRP tinggi
\ndengan fungsi kognitif dimana nilai p = 0,660 dan OR sebesar 1,214 (CI 95%,
\n0,512 – 2,882).
\nKesimpulan
\nTidak ada hubungan antara kadar hs-CRP tinggi dan fungsi kognitif yang di ukur
\ndengan MoCA-INA pada pasien lansia dengan DM tipe 2 di RSUD Dr. Soetomo
\nSurabaya.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0040.001
Scholarly communication0.0030.009
Open science0.0030.001
Research integrity0.0020.003
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 head0.026
GPT teacher head0.220
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

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