HUBUNGAN DERAJAT KLINIS DAN GANGGUAN KOGNITIF PADA PENDERITA PARKINSON DENGAN MENGGUNAKAN MONTREAL COGNITIVE ASSESMENT VERSI INDONESIA (MOCA-INA)
Bibliographic record
Abstract
Identifikasi secara dini gangguan kognitif pada penyakit Parkinson sangat penting, karena sangat mempengaruhi kualitas hidup penderita Parkinson. Hal-hal yang menjadi faktor risiko terjadinya gangguan kognitif masih sangat bervariasi, salah satu diantaranya adalah stadium lanjut penyakit. Penelitian ini bertujuan untuk mengetahui hubungan antara derajat klinis Parkinson dan gangguan kognitif dengan menggunakan tes Montreal Cognitive Assessment versi Indonesia ( MoCA-Ina). Desain penelitian adalah Cross Sectional Study, pada 37 subjek penderita Parkinson di Poli penyakit saraf Rumah Sakit Wahidin Sudirohusodo dan jejaringnya di Makassar, dari bulan Januari hingga Mei 2013. Pada subjek penelitian dilakukan pemeriksaan fungsi kognitif menggunakan instrument tes MoCA-Ina. Hasil penelitian menunjukkan jenis kelamin laki-laki lebih banyak dibanding perempuan pada penderita penyakit Parkinson (67,6% vs 32,4%). Hubungan antara gangguan kognitif dengan beberapa faktor risiko antara lain jenis kelamin, kelompok umur, hipertensi, DM, dislipidemia, durasi sakit dan depresi tidak didapatkan perbedaan yang bermakna. Dengan uji chi-square didapatkan hubungan yang bermakna antara derajat klinis Parkinson dan gangguan kognitif, dengan nilai p 0,003. Unsur kongnitif yang paling banyak terganggu adalah fungsi eksekutif dan atensi. Penelitian ini menerangkan bahwa semakin berat derajat klinis penyakit Parkinson semakin besar kejadian gangguan kognitif.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".