HUBUNGAN SKORMINI-MENTAL STATE EXAMINATION (MMSE) DAN SKOR MONTREAL COGNITIVE ASSESSMENT-VERSI INDONESIA (MOCA-INA) TERHADAP USIA DAN LAMA PENDIDIKAN PENERIMA MANFAAT PANTI SOSIAL TRESNA WERDHA (PSTW) GAU MABAJI GOWA, SULAWESI SELATAN TAHUN 2017
Bibliographic record
Abstract
Increasing human life expectancy worldwide increases the status of age-old cognitive impairment. Therefore, sensitive tests are required to overcome these cognitive disorders. This observational analytical study used the Mini-Mental State Examination ( MMSE) and the Montreal Cognitive Assessment (MoCA-Ina) to assess the cognitive impairment of the beneficiaries of the Panti Sosial Tresna Werdha (PSTW) Gau Mabaji Gowa, South Sulawesi regarding age and duration of education. A total of 55 samples were determined successively, the researchers found a significant mean relationship between age with MMSE score (p = 0,001) vs MoCA-Ina score (p = 0,030) and between education to MMSE score (p = 0,00) vs MoCA-Ina (p = 0.00) Keyword : MMSE, MoCA-Ina, age, education Peningkatan angka harapan hidup manusia di seluruh dunia meningkatkan insiden gangguan kognitif usia tua. Oleh karena itu, diperlukan tes yang sensitif untuk mendeteksi gangguan kognitif tersebut. Penelitian analitik observasional ini menggunakan Mini-Mental State Examination (MMSE) dan Montreal Cognitive Assessment- versi Indonesia (MoCA-Ina) untuk menilai gangguan kognitif penerima manfaat Panti Sosial Tresna Werdha (PSTW) Gau Mabaji Gowa, Sulawesi Selatan terkait usia dan lama pendidikan. Total 55 sampel yang ditentukan secara consecutive sampling , peneliti menemukan hubungan yang secara signifikan bermakna antara usia dengan skor MMSE ( p =0.001) vs MoCA-Ina ( p =0.030)dan antara lama pendidikan terhadap skor MMSE ( p =0.00) vs MoCA-Ina ( p =0.00) Kata kunci : MMSE, MoCA-Ina, usia, pendidikan
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".