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Record W2748037155 · doi:10.30867/action.v1i1.5

Pengaruh Pemberian Jus Alpukat Terhadap Tekanan Darah Penderita Hipertensi Di Batoh Wilayah Kerja Puskesmas Lueng Bata Kota Banda Aceh

2016· article· id· W2748037155 on OpenAlexaff
Indah Yusra, Aripin Ahmad, Agus Hendra Al Rahmad

Bibliographic record

VenueAcTion Aceh Nutrition Journal · 2016
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

Hipertensi merupakan faktor resiko utama dari perkembangan (penyebab) penyakit jantung dan stroke. Tekanan darah diastolik adalah tekanan darah pada saat jantung mengembang dan menyedot darah kembali. Prevalensi hipertensi secara nasional sebesar 31,7%, di Aceh prevalensi hipertensi mencapai 9,2%, di kota Banda Aceh prevalensi hipertensi mencapai 33,1%. Tujuan penelitian mengetahui pengaruh Pemberian Jus Alpukat Terhadap Tekanan Darah Di Desa Batoh. Penelitian bersifat deskriptif analitik, dengan rancangan quasi eksperimental, sampel dalam penelitian penderita hipertensi berjumlah 20 orang secara metode purposive sampling. Uji statistik digunakan yaitu T-Test Dependen pada CI:95%. Hasil penelitian bahwa rerata tekanan darah sampel sebelum pemberian jus alpukat adalah 95,75 mmHg dan Rata-rata tekanan darah sesudah pemberian jus alpukat adalah 83,25. Terdapat selisih penurunan tekanan darah 12,5 mmHg. Terdapat pengaruh yang signifikan pemberian jus Alpukat terhadap penurunan tekanan darah, P =0,000 (P≤0,05). Kesimpulan yaitu pemberian tritmen jus alpukat berpengaruh terhadap penurunan tekanan darah. Diharapkan kepada masyarakat agar dapat mempertimbangkan untuk mengkonsumsi jus alpukat untuk menurunkan tekanan darah pada penderita hipertensi.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.004
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.050
GPT teacher head0.315
Teacher spread0.265 · 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 designNot applicable
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

Citations3
Published2016
Admission routes1
Has abstractyes

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