Influencing factors of mild cognitive impairment in silent cerebral infarction
Bibliographic record
Abstract
Objective To analyze the influencing factors of mild cognitive impairment in silent cerebral infarction(SCI).Methods 82 patients with SCI were carried out minimental state examination(MMSE)and the Chinese version of the Montreal cognitive assessment(MOCA).The incidence of mild cognitive impairment were compared between the two scales.According to the testing results,patients were divided into mild cognitive impairment(MCI)group and non-cognitive impairment(NCI)group.All patients were evaluated about the general state of health and laboratory examination.Hamilton depression rating scale(HAMD)was used to test depression.We observed brain photographic classification.Carotid arteries(CAS)was assessed by two dimensional color Doppler ultrasonography in two groups.Results The sensitivity of MoCA was higher than MMSE(MoCA 41.46%,MMSE12.20%,P0.01).Depressions in MCI group were more than those in NCI group(P0.01).The lesions of frontal,temporal,subcortical and thalamus in MCI group were more than those of NCI group.Inner diameters of CCA and ICA were different between the two groups.The carotid intima-media thickness of patients with mild cognitive impairment was thicker than that of the NCI group.There were differences between the two groups on plaque instability of carotid(P0.01).Conclusion MoCA is more sensitive than MMSE on detection of patients with MCI.MoCA has clinical significance on detecting MCI.MCI is very common in patients of SCI.The occurrence of MCI is significantly correlated with sides and locations of the lesions,inner diameters of CCA and ICA,IMT,plaque stability,and depression.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 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".