ASSECSSING MOCA TEST IN SUBACUTE STROKE PATIENTS AND COMPARE MOCA VERSUS MMSE
Bibliographic record
Abstract
Background: Dementia after stroke, particularly subacute period is often overlooked. Today the quality of human life is increasingly high, finding scales that have high value for detection of dementia in patients with stroke is increasingly interested. MoCA test is high sensitivity with mild dementia and identify more abnormalities of awareness caused by vascular, but MoCA have not been studied much in Vietnam. Objective: Assessing MoCA test in subacute stroke patients and compare MoCA versus MMSE in these patients. Subjects: 90 patients with subacute stroke period, these people are being treated at Department of cardiovascular internal medicine at Hue Central Hospital, from 7/2014 - 7/2015. Methods: cross-sectional description and analysis. Results: The mean age is 65.57 ± 13.38, accounting for 54.4% male and 45.6% female. Age, duration of illness has weak correlation with MoCA. The risk factors: hypertension, stroke ischemic transient, alcoholism, smoking, heart disease, diabetes, dyslipidemia related no statistical significance with MoCA. The proportion of dementia in subacute stroke according MoCA is 82.2%. The concordance between MoCA and MMSE was good (kappa = 0.684). Using DSM-IV criteria as the gold standard we found MoCA more valuable in the dementia diagnosis than MMSE (AUC 0.864 versus 0.774, p <0.05). Conclusion: The rate of dementia in stroke subacute period according MoCA is quite high. MoCA is valuable than MMSE in detecting dementia in patients with stroke subacute period, this scale is short, easy to implement so should put into using widely in clinical practice. Key words: MoCA test, subacute stroke, dementia
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".