ADVANCED BLOOD SUGAR EXAMINATION FOR ELDERLY GROUPS AT THE CITRA LESTARI MIDWIFE PRACTICE, BOJONGGEDE DISTRICT, BOGOR REGENCY
Bibliographic record
Abstract
Age is a risk factor for Diabetes that cannot be avoided, older age has a greater risk for developing Diabetes. The impact of Diabetes does not only affect the quality of life of the elderly in general, but also their families and countries economically. In the community service activities previously carried out at the Citra Lestari Midwife Practice, Bojonggede District, Bogor Regency, the average blood glucose level of elderly participants was included in the Prediabetes category. This means that it has exceeded the normal average but has not met the criteria for Diabetes. During the Covid-19 pandemic, the biggest challenge faced by the elderly was reduced physical activity which increased the risk of developing Prediabetes to Diabetes, and limited access to health facilities to have their blood sugar levels checked regularly. Gunadarma University Midwifery Study Program in collaboration with the Citra Lestari Midwife Practice helps the surrounding elderly to be able to keep their blood sugar levels checked regularly. The results showed a decrease in blood glucose levels from the previous examination 144 mg / dl to 119 mg / dl at this examination. Providing health information regarding a balanced diet, paying attention to sugar intake, and maintaining physical activity and doing health exercises for Diabetes, is proven to be able to control glucose levels in the elderly group.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".