GAMBARAN FUNGSI KOGNITIF DENGAN INA-MoCA DAN MMSE PADA PENDERITA POST-STROKE DI POLIKLINIK SARAF BLU RSUP KANDOU MANADO NOVEMBER - DESEMBER 2014
Bibliographic record
Abstract
Abstract: In stroke patients there are damages of neurons resulting in disabilities of sensoric, motoric, and cognitive functions. Evaluation of cognitive function is needed to determine the level of functional ability that is useful in management and prognosis. This study aimed to obtain the cognitive function of post-stroke patients in Neurology Clinic Prof. Dr. R. D. Kandou Hospital Manado form November-December 2014 by using INA-MoCA and MMSE. This was a descriptive study with a cross sectional design. There were 35 patients as samples. The results showed that most of the patients were males (48.57%), age group 56-65 years old (37.1%), high school education (45.7%), and ischemic type stroke (97.1%). There were 97.1% of patients with INA-MoCA score <26. Moreover, there were 91.4% of patients with normal MMSE score, 5.7% probable, and 2.9% definite.Keywords: cognitive function disturbance, INA-MoCA, MMSE, post strokeAbstrak: Pada pasien stroke terjadi kerusakan sel-sel neuron yang dapat berakibat kecacatan fungsi sensoris, motoris, maupun kognitif. Evaluasi fungsi kognitif sangat diperlukan untuk menentukan tingkat kemampuan fungsional yang berguna untuk penanganan dan prognosis. Penelitian ini bertujuan untuk mendapatkan gambaran fungsi kognitif yang diperiksa dengan INA-MoCA dan MMSE pada penderita post-stroke di poliklinik saraf BLU RSUP Prof. Dr. R. D. Kandou Manado. Penelitian ini menggunakan metode deskriptif dengan desain potong lintang. Sampel berjumlah 35 pasien dengan karakteristik populasi paling banyak laki-laki 48,57%, kategori umur 56-65 tahun 37,1%, tingkat pendidikan SMA 45,7%, tipe stroke iskemik 97,1%. Pasien dengan skor INA-MoCA <26 sejumlah 97,1%. Dengan skor MMSE terdapat 91,4% pasien Normal, 5,7% Probable, dan 2,9% Definite.Kata kunci: gangguan fungsi kognitif, INA-MoCA, MMSE, post stroke
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".