GAMBARAN FUNGSI KOGNITIF PADA PASIEN DENGAN KADAR FERITIN SERUM RENDAH DI RUMAH SAKIT UMUM DAERAH DR. ZAINOEL ABIDIN BANDA ACEH
Bibliographic record
Abstract
Pemeriksaan yang paling sensitif untuk menilai defisiensi besi adalah feritin serum. Kadar feritin serum yang rendah menggambarkan penurunan cadangan besi tubuh. Kadar feritin serum yang rendah berhubungan dengan menurunnya proses metabolik yang mempengaruhi fungsi otak sehingga dapat menyebabkan gangguan fungsi kognitif. Penelitian ini bertujuan untuk mengetahui gambaran fungsi kognitif pada pasien dengan kadar feritin serum rendah di Rumah Sakit Umum Daerah dr. Zainoel Abidin Banda Aceh. Jenis penelitian ini adalah deskriptif dengan rancangan cross sectional. Pengambilan sampel dari tanggal 19 Oktober sampai dengan 11 Desember 2017. Subjek penelitian dikumpulkan dengan metode accidental sampling. Kadar feritin serum didapatkan dari hasil pemeriksaan laboratorium dan fungsi kognitif dinilai menggunakan kuisioner Montreal Cognitive Assesment versi Indonesia (MoCA-Ina). Hasil penelitian didapatkan 30 subjek penelitian yang memenuhi kriteria inklusi. Berdasarkan penelitian ini didapatkan kasus kadar feritin rendah paling banyak pada usia ?56 tahun (30%), jenis kelamin wanita (60%), tingkat pendidikan terakhir tamat SMA (40%), dan IRT (26,6%). Kadar feritin paling banyak yaitu feritin serum sangat rendah ?12 ng/mL (86,67%). Pasien dengan kadar feritin rendah yang memiliki fungsi kognitif normal 13,34% sedangkan yang mengalami gangguan fungsi kognitif 86,66%. Gangguan kognitif pada pasien dengan kadar feritin serum rendah bertambah seiring dengan kadar feritin sangat rendah ?12 ng/mL (88,5%), bertambahnya usia (85,5%), jenis kelamin pria (91,7%), dan tingkat pendidikan yang lebih rendah (66,7%).Kata kunci: Kadar feritin serum rendah, fungsi kognitif, MoCA-Ina
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".