<p>Analysis of correlation between cerebral perfusion and KIM score of white matter lesions in patients with Alzheimer’s disease</p>
Bibliographic record
Abstract
Purpose: This study aimed to characterize white matter lesions (WMLs) and regional cerebral perfusion, and evaluate their correlations with cognitive deficits in Alzheimer’s disease (AD) patients. Patient and methods: One hundred and twenty-eight patients with AD (AD group) and 75 subjects without AD (control group) were recruited. The medical information was collected from each subject. Montreal cognitive assessment (MoCA) was employed for the assessment of cognition. Cranial MRI was performed, and the KIM scoring system was used to evaluate the white matter hyperintensity. The CT perfusion (CTP) imaging was employed to assess the whole cerebral perfusion, and the region of interest (ROI) was selected to determine the blood perfusion at different parts. Results: The education level and MoCA score in AD group were significantly lower than in control group ( P <0.001). The KIM score of juxtaventricular WML (JVWMLs) was significantly different between two groups ( P <0.05) and AD group showed a higher incidence of severe JVWML and periventricular WML (PVWMLs); in AD group, the total KIM score and KIM scores of JVWMLs, PVWMLs and deep WML (DWMLs) showed negative relationships with the MoCA score ( P <0.001). As compared to control group, the blood perfusion of either whole brain or different parts in the AD group reduced significantly ( P <0.05). In the AD group, there was a negative correlations of blood perfusion at JVWM and PVWM with corresponding KIM scores ( P <0.05 or 0.01). In the AD group, the blood perfusions of the whole brain, JVWMLs, PVWMLs and deep WML were negatively related to MoCA score ( P <0.05). Conclusion: In conclusion, the cognitive deficits in the AD patients are associated with the degree of WMLs, especially the JVWML, PVWML and DWML as well as with the reduced perfusion of JVWM, PVWM and deep WM. Keywords: Alzheimer’s disease, cerebral blood flow, white matter lesion, KIM score
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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".