Clinical study of cognition assessment by MoCA in the patients with cerebral infarction
Bibliographic record
Abstract
Objective To observe the efficacy of the Chinese version of Montreal Cognitive Assessment Scale(MoCA) in cognition assessment in the patients with cerebral infarction and to evaluate its standard validity.Methods Sixty patients with cerebral infarction (stroke group) and 60 patients with diseases of peripheral nerves (control group) received the cognitive assessment by Mini-Mental State Examination(MMSE) and MoCA.In the stroke group,the cognitive assessment was performed again after one month of treatment.Results There was high correlation between the total scores of MMSE and MoCA in both groups before and after treatment (r = 0.921,r = 0.916,r = 0.899,P 0.01),and there was significant difference in the MoCA scores between the two groups (P 0.05).Compared with before treatment,the score of MoCA in the stroke group after treatment was higher except the item of delayed recall (P 0.05).In the stroke group,the rate of abnormal cognition assessment score by MoCA was higher than that by MMSE (x~2= 5.63,P 0.05).Conclusion The MoCA can effectively assess cognitive impairment and improvement with satisfactory standard validity in the patients with cerebral infarction. Compared with MMSE,the MoCA is a more sensitive screening tool for cognitive impairment in cerebral infarction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".