Cognitive functions and white matter lesions on magnetic resonance images in a sample of normal Iranian population with cardiovascular risk factors
Bibliographic record
Abstract
PURPOSE: Due to a suggestive three-way relationship between brain structural disorders, microvascular lesions, and cognitive impairments, we aimed to examine the association of the volume and number of white matter hyperintensity lesions and lacunar infarcts with cognitive impairment among patients with cardiovascular risk factors in a sample of the Iranian population. MATERIALS AND METHODS: This study was conducted on a total number of 156 normal subjects aged 30-74 years with cardiovascular risk factors. We used the Framingham general cardiovascular risk factors prediction model to calculate the likelihood of each risk factor. The total number of lacunar infarcts and the volume of white matter hyperintensity lesion were calculated in brain magnetic resonance imaging. Cognition status was assessed using the Montreal Cognitive Assessment questionnaire. RESULTS: An adverse association was revealed between Montreal Cognitive Assessment score and different cardiovascular risk profiles including the Framingham body mass index score ( p < 0.001) and the Framingham lipid score ( p < 0.001). The total volume of white matter hyperintensity was negatively associated with total Montreal Cognitive Assessment cognition score ( p < 0.001). Our study also showed an adverse association between total number of lacunar infarcts and total Montreal Cognitive Assessment cognition score ( p = 0.038) and with some cognition components including memory ( p = 0.013), attention ( p = 0.037), abstraction ( p = 0.046), and orientation ( p = 0.002). CONCLUSION: Periventricular lesions are associated with impaired memory, language, and visuoconstruction while subcortical lesions are associated with impairment in naming, attention, language, and abstraction functions in normal subjects with cardiovascular risk factors but without cardiovascular or cerebrovascular disorders.
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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 | 0.002 |
| 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".