A Correlation Study between the Changes of Cerebral Blood Flow and MMSE and MoCA Scores in Vascular Cognitive Impairment with No Dementia
Bibliographic record
Abstract
Aim To investigate the regional cerebral blood flow(rCBF) changes in vascular cognitive impairment with no dementia(VCIND) patients with single-photon emission computed tomography(SPECT) and to analyze its relationship with neuropsychological score. Methods 45 cases of VCIND group(male25, female 20) were recruited and 28 cases of stroke with normal cognition(SNC) group(male 16, female 12) were recruited, and 15 cases of healthy control(HC) group(male 6, female 9) were recruited. All subjectsunderwent a general examination, neuropsychological test, brain SPECT perfusion imaging. Results Compared with the HC group, the rCBF in the bilateral frontal lobe, bilateral superior parietal lobe and left thalamus in the VCIND group were significantly reduced(P0.05). The rCBF of the left frontal lobe, left inferior parietal lobe, left temporal lobe and bilateral thalamus and striatum were significantly reduced in SNC group(P0.05). The rCBF of the left frontal lobe, left superior parietal lobe and left thalamus were significantly lower in VCIND group than SNC group(P0.05). Within the VCIND group the rCBF of the left frontal, left superior parietal lobe, and left thalamus were significantly reduced compared with the right corresponding regions(P0.05). Correlation analysis in VCIND group showed that, Mini Mental State Examination(MMSE) score was positively correlated with the rCBF of the left frontal lobe, left superior parietal lobe, bilateral medial temporal lobe, and left striatum(r=0.73-0.84, P0.05). Montreal Cognitive Assessment(MoCA) score was positively correlated with the rCBF of left frontal lobe, right inferior parietal lobe, bilateral medial temporal lobe(r=0.65-0.85, P0.05). Conclusion SPECT is of great significance in detecting early rCBF changes in VCIND cases. MMSE and MoCA score have certain degree of correlation with rCBF changes.
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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".