KHASIAT MADU MENURUNKAN TEKANAN DARAH DAN HEMATOLOGI PARAMETER
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".