Gambaran skor MMSE dan MoCA-INA pada pasien cedera kepala ringan dan sedang yang dirawat di RSUP Prof. Dr. R. D. Kandou Manado
Bibliographic record
Abstract
Abstract: Traumatic Brain Injury (TBI) is the most common case in hospital. TBI can caused cognitive impairment. This study aimed to obtain the description of cognitive function in patients with mild and moderate TBI that were admitted to Prof. Dr. R. D. Kandou Hospital Manado. This was a prospective descriptive study by conducting direct examination to the patients diagnosed with mild or moderate TBI by using MMSE and Ina MoCA instruments. Thee results showed that there of 50 subjects there were 74% with mild TBI and 26% with moderate TBI. MMSE showed 96% normal while Ina MoCA showed 76% normal. Cognitive function impairment was more visible on Ina MoCA examination. Conclusion: Ina MoCA was better than MMSE examination in description of cognitive function impairment.Keywords: head injury, cognitive function Abstrak: Cedera kepala merupakan suatu kegawatan yang paling sering dijumpai di Rumah Sakit. Cedera kepala dapat menyebabkan gangguan fungsi kognitif. Penelitian ini bertujuan untuk mengetahui gambaran skor MMSE dan MoCA-Ina pada pasien cedera kepala ringan dan sedang yang dirawat di RSUP Prof. Dr. R. D. Kandou Manado. Jenis penelitian ini ialah deskriptif prospektif dengan melakukan pemeriksaan langsung pada pasien yang didiagnosis cedera kepala ringan atau sedang menggunakan instrumen MMSE dan MoCA-Ina. Hasil penelitian mendapatkan subjek sebanyak 50 orang dengan persentase cedera kepala ringan sebanyak 74% dan cedera kepala sedang 26%. Pada MMSE didapatkan 96% normal sedangkan pada MoCA-Ina didapatkan 76% normal. Penurunan fungsi kognitif lebih terlihat pada pemeriksaan MoCA-Ina. Simpulan: MoCA-Ina lebih dapat menggambarkan gangguan fungsi kognitif daripada pemeriksaan MMSE. Kata kunci: cedera kepala, fungsi kognitif
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".