KORELASI ANTARA LOKASI STROKE DENGAN GANGGUAN KOGNITIF PADA PENDERITA STROKE DI RSUP SANGLAH DENPASAR
Bibliographic record
Abstract
Latar Belakang Kasus stroke mengalami kecenderungan peningkatan baik dalam kematian maupun kecacatan. Morbiditas paska stroke dapat berupa masalah fisik, psikis dan kognitif. Risiko gangguan fungsi kognitif meningkat sebesar tiga kali lipat setelah suatu awitan stroke dan 25-50% diantaranya akan berkembang menjadi demensia paska stroke. Penilaian gangguan fungsi kognitif pada penderita paska stroke sangat penting oleh karena gangguan fungsi kognitif berhubungan dengan luaran fungsional yang buruk, tingkat ketergantungan yang tinggi, kualitas hidup yang rendah dan angka kematian yang tinggi. Hubungan lokasi lesi dengan gangguan kognitif pada stroke masih menunjukkan hasil yang berbeda. Metode Penelitian potong lintang pada penderita paska stroke yang dirawat di bangsal rawat inap dan poliklinik saraf RSUP Sanglah Denpasar mulai Oktober 2018 sampai Desember 2018 dengan menggunakan Montreal Cognitive Assessment Indonesian Version (MoCA-Ina). Hasil Responden yang mengikuti sebanyak 80 orang, didapatkan hasil gangguan kognitif pada 43 responden (53,8%), sedangkan sebanyak 37 responden (46,3%) tidak mengalami gangguan kognitif. Simpulan Uji korelasi koefisien kontingensi digunakan, didapatkan hasil analisis uji korelasi didapatkan bahwa lokasi stroke dan fungsi kognitif memiliki korelasi positif dengan kekuatan korelasi yang sedang (r=0,544) dan bermakna secara signifikan (p<0,001). Kata kunci: lokasi stroke, gangguan 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".