GAMBARAN KADAR GULA SESAAT PADA DEWASA MUDA USIA 20-30 TAHUN DENGAN INDEKS MASSA TUBUH (IMT) ≥ 23 kg/m2
Bibliographic record
Abstract
Abstract: Hyperglycemia is a state of elevated level of blood sugar in human body that exceeds normal level. The causes are not known yet for sure but it is often associated with insulin insufficiency and predisposition factors such as genetic, age, and obesity. Prolonged hyperglycemia may lead to the development of diabetes mellitus and as a risk factor of other metabolic diseases. Morbidity in hyperglycemia is increased along with the age and body weight. This study aimed to obtain the random blood glucose level among young adults aged 20-30 years with a body mass index (BMI) ≥23 kg/m2. This was a descriptive study. The population consisted of 20 to 39 years old young adults with body mass index (BMI) ≥23 kg/m2 who lived in the working area Community Health Center in Beo, Talaud. Data consisted of BMI measurements and random blood glucose levels by using stick device. The results showed that of 30 respondents with BMI ≥23 kg/m2, there was 1 respondent (3.33%) had hyperglycemia meanwhile the other 29 respondents (96.6%) had normal blood glucose level. Conclusion: In this study, the random blood glucose level among young adults aged 20-30 years with body mass index (BMI) ≥23 kg/m2 were in normal range.Keywords: random plasma glucose level, 20-30 years old young adults, BMI ≥ 3 kg/m2Abstrak: Hiperglikemia adalah keadaan peningkatan kadar glukosa darah dalam tubuh seseorang yang melebihi kadar normal. Penyebab belum diketahui pasti tetapi sering dihubungkan dengan kurangnya insulin dan fator predisposisi yaitu genetik, umur, dan obesitas. Hiperglikemia yang tidak dikontrol secara terus menerus akan berkembang menjadi penyakit diabetes mellitus dan merupakan faktor risiko untuk penyakit metabolik lainnya. Angka morbiditas pada hiperglikemia juga meningkat seiring bertambahnya umur dan berat badan. Penelitian ini bertujuan untuk mengetahui gambaran kadar glukosa darah pada dewasa muda yang berusia 20-30 tahun dengan Indeks Massa Tubuh (IMT) ≥23 kg/m2. Penelitian ini menggunakan metode deskriptif. Populasi ialah dewasa muda berusia 20-30 tahun dengan IMT ≥23 kg/m2 yang tinggal di wilayah Puskesmas Beo Kecamatan Beo Kabupaten Kepulauan Talaud. Data diperoleh dengan pengukuran IMT dan pemeriksaan kadar glukosa darah sesaat dengan menggunakan alat stick. Terdapat 30 responden dengan IMT ≥23 kg/m2 dimana 1 orang (3.33%) yang mengalami hiperglikemia dan 29 orang (96.6%) dengan kadar glukosa darah dalam batas normal. Simpulan: Sebagian besar dewasa muda usia 20-30 tahun dengan IMT ≥23 kg/m2 mempunyai kadar glukosa darah sesaat normal.Kata kunci: glukosa darah sesaat, dewasa muda usia 20-30 tahun, IMT ≥23 kg/m2
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
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 teacher head, 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".