The role of cholinergic pathway lesions in vascular cognitive impairment
Bibliographic record
Abstract
Objective To investigate the relationship between white matter lesions (WML) within the cholinergic pathway and vascular cognitive impairment.Method Middle-aged and elderly stroke patients underwent brain MRI examination and Montreal Cognitive Assessment (MoCA).Cholinergic Pathways Hyperintensities Scale (CHIPS) scores and the overall WML burden by Schelten on fluidattenuated inversion recovery MRI images were determined and compared with MoCA scores.Spearman partial rank correlation coefficients and standardized regression coefficients were calculated.Results Thirty four patients were included ( mean age ( 62.2 ± 8.8 ) years, 45-82 years).MoCA scores negatively correlated with WML burdens by Schelten scores ( β = - 0.357, P = 0.042) and CHIPS scores ( β =-0.382,P=0.026).CHIPS scores were negatively associated with visuospatial and executive function (r = - 0.290, P = 0.048 ), naming function ( r = - 0.486, P = 0.002 ), attention ( r = - 0.311, P =0.037) and abstraction ( r = - 0.344, P = 0.023).Schelten scores were negatively associated with naming function (r = - 0.492, P = 0.002), attention ( r = - 0.364, P = 0.017) and abstraction ( r = - 0.390,P=0.011).Conclusion WML lesions within the cholinergic pathyway play a possible role in vascular cognitive impairment especially in visuospatial and executive function. Key words: Cholinergic fibers; Neurol pathways; Brain ischemia; Stroke; Congnitive disorders; Severity of illness index
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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 | 0.001 | 0.003 |
| 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".