HUBUNGAN HIPERTENSI DENGAN GANGGUAN FUNGSI KOGNITIF PADA PASIEN POST-STROKE ISKEMIK DI RS BETHESDA
Bibliographic record
Abstract
Pendahuluan: Stroke bisa menimbulkan gangguan fungsi kognitif. Insidensi gangguan fungsi kognitif meningkat tiga kali lipat setelah stroke, dan biasanya melibatkan gangguan kemampuan visiospasial, memori, orientasi, bahasa, perhatian, dan fungsi eksekutif. Penelitian sebelumnya terkait hipertensi dan gangguan fungsi kognitif pada stroke masih kontroversial. Metode: Penelitian ini menggunakan metode potong lintang. Data yang diambil berupa data primer dengan menggunakan Montreal Cognitive Assessment versi Indonesia (MoCA-Ina) serta Clock Drawing Test (CDT) dan data sekunder dari Stroke Registry (2010-2017) dan rekam medis RS Bethesda Yogyakarta. Data yang didapatkan dianalisis secara deskriptif (univariat), dilanjutkan dengan uji chi-square test untuk analisis bivariat, dan regresi logistik digunakan untuk menganalisis analisis multivariat. Hasil: Sampel yang didapatkan sebanyak 110 sampel, terdiri dari 72 laki-laki (65%) dan 38 perempuan (34.5%), di mana usia terbanyak 51-60 tahun sebanyak 36 pasien (32.7%). Ada 75 pasien (68.2%) yang mengalami gangguan fungsi kognitif (MoCA 26) dan 35 pasien (31.8%) yang tidak mengalami gangguan fungsi kognitif (MoCA 26). Pada analisis bivariat didapatkan hipertensi (OR: 1.02; CI: 0.70-1.49; p: 0.823) tidak mempengaruhi terjadinya gangguan fungsi kognitif pada pasien post-stroke iskemik. Pada analisis multivariat didapatkan onset jumlah serangan stroke, jumlah lesi, lesi parietal, dan lesi berhubungan dengan gangguan fungsi kognitif post-stroke iskemik. Kesimpulan: Hipertensi tidak berhubungan dengan gangguan fungsi kognitif pada pasien post-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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".