Analisa Kadar Glutamat pada Penderita Fibrilasi Atrium dengan Gangguan Fungsi Kognitif
Bibliographic record
Abstract
Salah satu permasalahan neurologi yang ditemukan pada penderita fibrilasi atrium (FA) adalah gangguan kognitif. Silent Brain Infarction (SBI) diyakini menjadi salah satu mekanisme utama yang mendasari terjadinya gangguan ini. Sudah dilaporkan juga bahwa hipoksia serebri akan menimbulkan peningkatan kadar glutamat ektraseluler sehingga bersifat neurotoksisitas dan menimbulkan kematian sel. Tujuan: Menganalisis kadar serum glutamat pada pasien Fibrilasi Atrial (FA) dengan gangguan kognitif. Metode: Penelitian dengan disain potong lintang dilakukan di Poliklinik Kardiologi dan Neurologi RS DR M Djamil Padang serta Laboratorium Biomed Fakultas Kedokteran Universitas Andalas. Pemeriksaan kadar glutamat serum dilakukan dengan metode Elisa dan pemeriksaan fungsi kognitif dengan test neuropsikologi Montreal Cognitive Assestment versi Indonesia (MoCA-Ina). Perbedaan kadar glutamat serum pada kelompok FA dengan gangguan kognitif dan kelompok FA tanpa gangguan kognitif diuji dengan t test bila distribusi data normal dan test Mann Whitney bila data tidak terdistribusi normal. Hubungan antara kadar glutamat dengan kejadian gangguan kognitif dilakukan dengan uji Chi-square, setelah dicari dulu nilai cut off point untuk kadar glutamat serum. Uji dikatakan bermakna bila nilai p < 0,05. Hasil: Kadar glutamat serum kelompok FA dengan ganggan kognitif lebih tinggi dari kelompok FA tanpa gangguan kognitif. Pasien FA yang mempunyai kadar glutamat tinggi ( > 29,5µMol/L) beresiko mengalami gangguan kognitif 10,2 kali lebih tinggi dari penderita yang mempunyai kadar glutamat normal (< 29,5 µMol). Simpulan: Ada hubungan antara kadar glutamat serum dengan terjadinya gangguan kognitif pada penderita FA.Kata kunci: fibrilasi atrial, fungsi kognitif, glutamat, silent brain infarction
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".