HUBUNGAN PENGENDALIAN GULA DARAH DENGAN GANGGUAN FUNGSI KOGNITIF PADA PASIEN DIABETES MELITUS TIPE 2 DI POLIKLINIK PENYAKIT DALAM RSUP. DR. M. DJAMIL PADANG
Bibliographic record
Abstract
Diabetes Melitus (DM) merupakan suatu kelompok penyakit metabolik dengan karakteristik hiperglikemia yang terjadi karena kelainan sekresi insulin dan kerja insulin atau kedua-duanya. Diabetes Melitus tipe 2 berkaitan erat dengan peningkatan risiko disfungsi kognitif, demensia, dan depresi. Penelitian ini bertujuan untuk mengetahui hubungan pengendalian gula darah dengan gangguan fungsi kognitif pada pasien diabetes melitus tipe 2. \nPenelitian ini merupakan penelitian observasional analitik dengan pendekatan cross-sectional. Penelitian berlangsung bulan Februari 2020 – October 2020. Jumlah sampel penelitian ini 30 orang pada pasien DM tipe 2 di Poliklinik Penyakit Dalam RSUP Dr. M. Djamil Padang. Teknik pengambilan sampel menggunakan consecutive sampling. Data diperoleh dari rekam medis dan wawancara menggunakan kuesioner MoCA (Montreal Cognitive Assesment) melalui telepon dan media WhatsApp. Data dianalisis secara univariat dan uji Chi-Square. \nHasil penelitian ini menunjukkan bahwa 86,7% responden memiliki kadar gula darah yang tidak terkendali dan 73,3% responden mengalami gangguan fungsi kognitif. Terdapat hubungan signifikan antara pengendalian gula darah dengan gangguan fungsi kognitif (p=0,048). \nKesimpulan penelitian ini adalah terdapat hubungan yang signifikan antara pengendalian gula darah dengan gangguan fungsi kognitif.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.012 |
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".