Comparison of the Application Value of Mini-mental State Examination and Montreal Cognitive Assessment in Lacunar Infarction Patients with Cognitive Impairment
Bibliographic record
Abstract
Objective To compare the application value of mini-mental state examination(MMSE) and Montreal cognitive assessment(MoCA) in detecting cognitive impairment in lacunar infarction(LI) patients.Methods The LI patients were first screened by MMSE,and those having a normal education-adjusted score were further assessed by MoCA(26 as the cut-off score).The patients with MoCA score less than 26 were selected into cognitive impaired LI-CI group and the patients with more than 26 were classified as normal control LI-NC group.Fifty healthy individuals were recruited in the healthy control group(NC group).The total score and the score of each cognitive field of MoCA among three groups was compared and analyzed.Results 53%(50/94) LI patients with normal MMSE had MoCA 26.There was a statistical significant difference(P 0.01) of the cognitive function between these cases and patients with MoCA ≥ 26.The MoCA score of LI-CI group was less than LI-NC group in visuospatial,executive,naming,delayed recall,abstraction and so on.The differences were statistical significant(P 0.05).Conclusion The cognitive impairment of those patients with normal MMSE score but abnormal MoCA score were mainly in visuospatial,executive,naming,delayed recall,abstraction and so on.MoCA is more sensitive than MMSE in cognitive impairment screening in LI patients.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".