Hubungan Kadar Ureum dan Kreatinin Serum dengan Fungsi Kognitif Penderita Penyakit Ginjal Kronik
Bibliographic record
Abstract
Penyakit Ginjal Kronik (PGK) dapat menimbulkan berbagai komplikasi, salah satunya adalah gangguan fungsi kognitif. Faktor penyebabnya adalah akumulasi zat-zat toksik di dalam tubuh, termasuk ureum dan kreatinin serum. Tujuan penelitian ini adalah melihat hubungan kadar ureum dan kreatinin serum dengan fungsi kognitif penderita PGK. \nPenelitian analitik observasional dengan desain cross-sectional ini dilakukan kepada 46 pasien PGK yang dihemodialisa di RSUP Dr. M. Djamil Padang. Sampel diambil menggunakan teknik consecutive sampling. Fungsi kognitif dinilai menggunakan Montreal Cognitive Assessment versi Indonesia (MoCA-Ina). Kadar ureum dan kreatinin serum diukur dengan alat spektrofotometer. Data dianalisis menggunakan uji Chi-Square. \nHasil penelitian menunjukkan 52.2% pasien PGK mengalami gangguan kognitif. Gangguan kognitif lebih banyak pada usia di atas 50 tahun (54.2%) dan berjenis kelamin laki-laki (58.3%), sebagian besar menempuh pendidikan minimal pada jenjang SMA dan ke atas (70.8%), mayoritas disertai hipertensi (75%), dan tanpa diabetes melitus (91.7%). Median kadar ureum dan kreatinin serum pasien dengan gangguan kognitif adalah 124 mg/dL dan 10.05 mg/dL. Hubungan antara kadar ureum dan kreatinin serum dengan fungsi kognitif memiliki nilai p-value sebesar 0.039 dan 0.768. \nKesimpulan penelitian ini adalah terdapat hubungan yang bermakna antara kadar ureum serum dengan fungsi kognitif penderita PGK, tetapi tidak terdapat hubungan yang bermakna antara kadar kreatinin serum dengan fungsi kognitif penderita PGK.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".