HUBUNGAN ANTARA ATEROSKLEROSIS ARTERI SEREBRI MEDIA DENGAN GANGGUAN FUNGSI KOGNITIF PADA STROKE ISKEMIK DI RSUDZA BANDA ACEH
Bibliographic record
Abstract
Aterosklerosis merupakan penyebab utama stroke iskemik terutama padaarteri serebri media yang dapat menimbulkan penurunan fungsi kognitif. Tujuanpenelitian ini adalah untuk mengetahui hubungan antara aterosklerosis arteriserebri media dengan gangguan fungsi kognitif pada pasien stroke iskemik diRSUDZA Banda Aceh. Jenis penelitian ini adalah penelitian analitik denganrancangan cross sectional pada bulan Agustus hingga September 2013. Penelitianini menggunakan instrumen Transcranial Doppler (TCD), Montreal CognitiveAssessment versi bahasa Indonesia (MoCA-Ina), dan rekam medis. Sampel dalampenelitian ini sebanyak 34 orang. Berdasarkan hasil penelitian diperoleh hasil ujianalisis alternatif Chi Square yaitu uji Fisher yang menunjukkan terdapathubungan antara aterosklerosis arteri serebri media dengan gangguan fungsikognitif (p = 0,05) pada pasien stroke iskemik di RSUDZA Banda Aceh. Proporsigangguan fungsi kognitif didapatkan sebesar 91,2% dan domain kognitif yangdominan terganggu adalah memori sebesar 70,6%. Pasien stroke iskemik yangpaling banyak mengalami gangguan fungsi kognitif berada pada kelompok usia55-65 tahun (95%), tamatan SMA (100%), dan memiliki lesi subkorteks (85,7%).Proporsi gambaran TCD arteri serebri media yang paling tinggi adalahaterosklerosis arteri serebri media kanan dan kiri yaitu sebesar 32,4%. Pasienstroke iskemik yang paling banyak memiliki gambaran TCD abnormal adalahpasien dengan lesi subkorteks (92,9%) dan lesi multipel (92,3%).Kata Kunci: Transcranial Doppler, Montreal Cognitive Assessment,aterosklerosis arteri serebri media, gangguan fungsi kognitif,stroke iskemik
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".