Application Value of Montreal Cognitive Assessment in Lacunar Infarction Patients with Cognitive Impairment
Bibliographic record
Abstract
Objective To analyze the application value of Montreal Cognitive Assessment(MoCA)(Chinese version) in detecting cognitive impairment in patients with lacunar infarction(LI).Methods The patients confirmed with LI were first screened by Mini-Mental State Examination(MMSE),and patients having normal MMSE score were further assessed by MoCA after education adjustment(26 as the cut-off score).The patients with MoCA score less than 26 were selected as cognitive impaired LI-CI group and the patients with more than 26 were selected as normal control LI-NC group.MoCA score,MMSE score and scores of each cognitive field of MoCA were compared between the two groups.Results 53%(50/94) LI patients with normal MMSE score had MoCA socre26,and these patients′ cognitive function showed statistically significant difference with the patients who had MoCA socre≥26(P0.01).The scores of visuospatial and executive function,naming,Abstract and delayed recall of the LI-CI group showed statistically significant differences with those of the LI-NC group(P0.05).Conclusion MoCA is more sensitive than MMSE in screening cognitive impairment in LI patients.The cognitive impairments of patients with normal MMSE but abnormal MoCA are mainly visuospatial and executive function,naming,delayed recall,Abstract and so on.
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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 | 0.004 |
| 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) | 0.001 | 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".