HUBUNGAN KOLESTEROL NON-HDL TERHADAP FUNGSI KOGNITIF PADA PASIEN STROKE ISKEMIK DENGAN DEFISIT NEUROLOGIS RINGAN
Bibliographic record
Abstract
Pendahuluan: Pasien stroke iskemik memiliki risiko menderita gangguan kognitif. Derajat gangguan kognitif antara lain dipengaruhi tingkat defisit neurologis. Derajat gangguan kognitif juga dipengaruhi oleh kadar kolesterol non-high density lipoprotein (non-HDL) yang abnormal karena mengandung semua lipid aterogenik yang menyebabkan terjadinya aterosklerosis. Tujuan: Mengetahui hubungan kadar kolesterol non-HDL terhadap gangguan kognitif pasien stroke iskemik terhadap defisit neurologis ringan di Rumah Sakit Atma Jaya pada tahun 2014-2018. Metode: Penelitian potong lintang dengan menganalisis rekam medis pasien (115 subjek). Analisis data menggunakan uji Chi-square dan uji Fisher. Hasil: Jumlah sampel pada penelitian ini adalah 115 orang. Rerata usia adalah 58±11,286 tahun. Tingkat pendidikan dengan proporsi tertinggi adalah Sekolah Menengah Atas (SMA), yaitu 30,4%. Berdasarkan Mini Mental State Examination (MMSE), 44 sampel (38,3%) mempunyai gangguan kognitif, dan 79,5% di antaranya memiliki kadar kolesterol non-HDL abnormal. Sementara itu, berdasarkan Montreal Cognitive Assessment Versi Indonesia (MoCA-Ina), 81 sampel (70,4%) mempunyai gangguan kognitif, dan 76,5% di antaranya memiliki kadar kolesterol non-HDL abnormal. Kolesterol non-HDL tidak berhubungan signifikan dengan fungsi kognitif yang diukur baik dengan MMSE maupun MoCA-Ina. Diskusi: Penelitian ini bertentangan dengan penelitian sebelumnya. Hal ini disebabkan oleh tidak ada hubungan secara langsung antara kolesterol non-HDL dan fungsi kognitif. Selain itu, kolesterol berperan sebagai antioksidan yang membantu transmisi sinyal saraf dan bersifat neuroprotektor. Kata kunci: Defisit neurologis ringan, fungsi kognitif, kolesterol non-HDL, stroke iskemik
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.026 | 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".