Bibliographic record
Abstract
Objective To study the correlation between the presence and number of cerebral microbleeds(CMB)with cognitive function in cerebral small vessel disease(CSVD)patients without dementia.Methods Of the 156CSVD patients without dementia who underwent T2-weighted gradient echo MRI,28CMB positive patients served as a positive group and 28gender-,age-and education-matched CMB negative patients served as a control group.Their cognitive function was scored according to the MMSE and MoCA(Chinese version),respectively.Results The total MMSE and MoCA score were significantly lower while the incidence of brain white matter lesion and lacunar infarction was significantly higher in CMB positive group than in control group(P= 0.000,P=0.001).The CMB variants were negatively related with the the total MMSE and MoCA score,visuospatial and executive performance,calculation and attention(r=-0.778,P= 0.000;r=-0.783,P=0.000;r=-0.591,P=0.003;r=-0.539,P=0.008),and not related with naming(P=0.197),language(P=0.064),abstraction(P=0.288),memory(P=0.124), and orientation(P=0.157)when the gender,age,education years,white matter lesion and lacunar infarction were used as strain variants.Conclusion The number of CMB is related with vascular cognitive impairment(VCI)and CMB can thus be used as a biomarker in early diagnosis of VCI.
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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 | 0.001 |
| 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".