HUBUNGAN KADAR GULA DARAH DENGAN FUNGSI KOGNITIF PASIEN STROKE ISKEMIK DIUKUR DENGAN MONTREAL COGNITIVE ASSESSMENT VERSI INDONESIA
Bibliographic record
Abstract
ABSTRAKStroke adalah salah satu penyakit kardiovaskular yang menjadi penyebab kematian dan kecacatan jangka panjang, gangguan fungsi kognitif, dan demensia. Penurunan fungsi kognitif meningkat tiga kali lipat paska stroke. Salah satu faktor risiko yang dapat meningkatkan gangguan fungsi kognitif adalah kadar gula darah. Tujuan penelitian ini adalah untuk mengetahui hubungan kadar gula darah dengan fungsi kognitif pasien stroke iskemik. Jenis penelitian ini adalah analitik observasional dengan pendekatan crossectional. Pengumpulan data dilakukan pada bulan September-November 2017 di Rumah Sakit Umum daerah dr Zainoel Abidin Banda Aceh. Pengambilan sampel dilakukan dengan menggunakan teknik consecutive sampling dengan jumlah sampel 47 orang. Pengambilan data dilakukan dengan penilaian fungsi kognitif menggunakan Montreal Cognitive Assessment versi Indonesia (MoCA-Ina) dan wawancara. Jumlah pasien stroke iskemik dengan normoglikemi sebanyak 33 orang (70,2%) dan jumlah pasien stroke iskemik dengan hiperglikemi sebanyak 14 orang (29,8%). Rata-rata skor MoCA-Ina berdasarkan KGDS, pasien dengan normoglikemi yaitu 15,4 sedangkan pasien dengan hiperglikemi yaitu 15,6. Berdasarkan hasil analisis dengan Uji Korelasi Spearman, didapatkan nilai signifikansi p= 0,502 dan nilai koefiesien korelasi (rs) 0,10, sehingga secara statistik dapat disimpulkan bahwa tidak terdapat hubungan antara kadar gula darah dengan fungsi kognitif pasien stroke iskemik yang diukur dengan Montreal Cognitive Assessment versi Indonesia (MoCA-Ina).Kata Kunci : Stroke Iskemik, Kadar Gula Darah, Hiperglikemi, Normoglikemi, Fungsi Kognitif, MoCA-Ina
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".