Korelasi Angka Leukosit Dengan Skor Mini-Mental State Examination (MMSE) Dan Skor Montreal Cognitive Assessment Versi Indonesia (MoCA-INA) Pada Pasien Cedera Kepala
Bibliographic record
Abstract
Cedera kepala langsung maupun tidak langsung dapat mengakibatkan gangguan fungsi kognitif. Gangguan fungsi kognitif jangka panjang dan tidak dilakukan penanganan optimal dapat mempengaruhi kemandirian dan mengganggu aktifitas sehari-hari bahkan menyebabkan penurunan kualitas hidup. Penelitian ini dilkaukan untuk mengetahui gambaran angka leukosit, fungsi kognitif (skor MMSE dan skor MoCA-Ina) dan derajat cedera kepala pada pasien cedera kepala. Digunakan penelitian analitik observasional dengan desain potong lintang. Sampel penelitian adalah 49 pasien cedera kepala yang dirawat di ruang rawat neurologi RSUD Dr. Zainoel Abidin Banda Aceh. Pemilihan sampel menggunakan teknik consecutive sampling. Instrumen penelitian terdiri atas Mini-Mental State Examination (MMSE) dan Montreal Cognitive Assesment versi Indonesia (MoCA-INA). Uji hipotesis yang digunakan adalah uji Chi Square. Didapatkan hasil bahwa, sebanyak 28 pasien dengan angka leukosit >11.000/ µL (57,1%), 37 pasien dengan fungsi kognitif normal berdasarkan skor MMSE (75,5%), 47 pasien dengan gangguan fungsi kognitif ringan berdasarkan skor MoCA-Ina (47%), dan 22 pasien dengan cedera kepala berat (44,9%). Hasil penelitian ini menunjukkan tidak terdapat korelasi antara angka leukosit dengan skor MMSE (p= 0,675) dan skor MoCA-INA (p= 0,409). Secara umum dapat disimpulkan bahwa tidak terdapat hubungan antara-angka leukosit dengan skor Mini-Mental State Examination (MMSE) dan skor Montreal Cognitive Assessment Versi Indonesia (MoCA-INA) pada pasien cedera kepala.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".