Neuropsychological research and ApoE genotype polymorphism analysis in mild cognitive impairment
Bibliographic record
Abstract
ObjectiveTo explore the differences of patients with mild cognitive impairment (MCI) and normal elderly individuals in neuropsychology and ApoE genotype polymorphism.Methods23 patients with MCI and 28 normal controls were examined with the neuropsychological test and ApoE phenotypes. The neuropsychological test included mini-mental state examination (MMSE), activities of daily living scale (ADL), Preffer outpatient disability questionnair (POD), Fuld object memory evaluation (FOM), rapid verbal retrieve (RVR), digit span (DS), logical memory (LM), Geometry figures, clock drawing test (CDT), delayed recall, clinical dementia rating scale (CDR), global deterioration scale (GDS), Hachinski ischaemic scale (HIS) and center for epidemiological studies-depression scale (CES-D).ResultsMCI cases achieved significantly lower scores than healthy elderly in all cognitive function measures ( P0.05～0.001)except ADL,POD performance and naming, especially in logical memory and semantic memory, similar with the earlier period of AD. The ApoE genotype polymorphism examination showed that the rate of ApoE epsilon 4 allele carry in MCI patients was around 10 times to that in normal controls.ConclusionIndividuals with MCI appear to be at an increased risk of developing AD. Susceptible neuropsychological marker binding other biomarker, e.g. ApoE, can raise the sensibility and specificity in early diagnosis of AD. [
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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 | 0.001 |
| 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".