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Record W2761302795 · doi:10.30597/mkmi.v13i1.1586

KHASIAT MADU MENURUNKAN TEKANAN DARAH DAN HEMATOLOGI PARAMETER

2017· article· id· W2761302795 on OpenAlexaff
Nurhaedar Jafar, Sitti Khadijah Hamid, Citrakesumasari Citrakesumasari, Ulfa Najamuddin, Aminuddin Syam

Bibliographic record

VenueMedia Kesehatan Masyarakat Indonesia · 2017
Typearticle
Languageid
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

Angka kejadian Diabetes Melitus (DM) terus mengalami peningkatan, baik di dunia, regional, maupun di Indonesia. Salah satu penyebab DM adalah stres oksidatif, sehingga madu sebagai antioksidan alami mampumenurunkan komplikasi yang terjadi. Penelitian ini bertujuan menilai efek pemberian madu terhadap tekanan darah dan hematologi parameter. Jenis rancangan yang digunakan adalah quasi experiment dengan desain prepost control. Populasi adalah penderita DM tipe 2 di dua wilayah kerja puskesmas di Kota Makassar. Sampel adalah sebagian yang memenuhi kriteria inklusi sebanyak 36 penderita DM tipe 2 yaitu 18 responden kelompok intervensi (KI) (diberikan madu dan edukasi gizi), dan 18 responden kelompok kontrol (KII) (diberikan edukasi gizi). Analisis data menggunakan uji Wilcoxon dan uji Mann-Whitney U. Hasil penelitian yaitu terjadi penurunan signifikan kadar kolesterol total, LDL, Gula Darah Puasa (GDP) dan Tekanan Darah Diastolik (TDD) pada KI. KII terjadi penurunan signifikan pada kadar LDL dan GDP, tetapi terjadi peningkatan signifikan pada TDD. Terjadi perbedaan yang signifikan pada TDD antara KI dengan KII (p=0,04). Pemberian madu memberi perubahan positifpada GDP (26,2%), total kolesterol (8,3%), LDL (11,7%), HDL (5,4%), tekanan darah sistolik (TDS) (4,1%) dan TDD (9,1%). Kesimpulan dari penelitian bahwa madu menurunkan kadar GDP, kolesterol total, LDL, TDD dan TDS pada penderita DM tipe 2.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.003

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 head0.034
GPT teacher head0.291
Teacher spread0.258 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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