Study of MMSE,MoCA and ERP of early cognitive function of multiple lacunar infarction
Bibliographic record
Abstract
Objective: To explore relevance and consistency of ERP-P300 and MoCA,MMSE in the early cognitive assessment of multiple lacunar infarction.Methods: 100 cases by CT or MRI scan shows multiple lacunar cerebral infarction(experimental group) and 100 cases of control group were examined by P300 and MoCA,the MMSE respectively,early cognitive dysfunction cutoff standards of the two groups were formulated,detection rates of cognitive impairment were compared with early cognitive dysfunction in two groups.Results: ①compared with the control group,the experimental group P300 latency prolonged,the difference was statistically significant(P 0.05);between the two groups P300 amplitude,the difference was not statistically significant(P 0.05).Extend experimental group with mild cognitive dysfunction than normal P300 latency,the difference was statistically significant(P 0.05);difference between the two sets of P300 amplitude was not statistically significant(P 0.05).②Compared with the control group,the experimental group MMSE and MoCA scores were lower than the control group,the difference was statistically significant(P 0.001).Experimental group cognitive dysfunction MMSE and MoCA scores were lower than normal,the difference was statistically significant(P 0.001).③ the comparison of different screening tools and demarcation criteria for mild vascular cognitive impairment detection rate in the experimental group than the control group,the difference was statistically significant(P 0.001);detection rate of the experimental group sort were: MoCA86.6%(84 /97),P300-Ⅱ48.5%(47 /97),MMSE37.1%(36 /97),P300-Ⅰ25.8%(25 / 97).④The experimental group was negatively correlated with P300 latency and MMSE,MoCA scores(P 0.05 ～ P 0.01);no correlation between P300 amplitude and MMSE,MoCA scores;between MMSE and MoCA scores were positively correlated(P 0.001).Conclusions: mVIC is a common complication of multiple patients with lacunar infarction;P300 latency as evaluating cognitive dysfunction a more objective electrophysiological indicators mVCI certain clinical diagnostic value;MoCA demarcation value as a multiple lacunarthe cerebral infarction mVCI screening standards;MMSE and P300 latency demarcation value can be used as the multi hair a lacunar infarction mVCI diagnosis reference standard.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".