Analysis of the correlations between the extracranial internal carotid artery and extracranial vertebral artery and mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Vascular tortuosity is a prevalent morphological change that frequently occurs in arteries across different parts of the body. OBJECTIVE: To analyze the relationship between the tortuosities of the extracranial internal carotid artery (EICA) and extracranial vertebral artery (EVA) with mild cognitive impairment. METHODS: The tortuosity index (TI), vascular deviation degree, tortuosity degree, and angle number of the EICA and EVA were retrospectively analyzed and calculated in 160 patients who underwent computed tomography angiography (CTA) in this study's department, and the Montreal cognitive assessment was adopted to evaluate the cognitive function of the patients. RESULTS: The differences in age, gender, arterial hypertension (AH), and diabetes mellitus (DM) between the normal group and the mild cognitive impairment group were statistically significant (p< 0.01). The TI was negatively correlated with the score of cognitive function. The tortuosities of the EICA and EVA were correlated with mild cognitive impairment (p< 0.05). The reduction in visual-spatial ability was correlated with the right EICA tortuosity, and the reduction in memory was correlated with the EVA tortuosity. Age, gender, HP, DM, and coronary heart disease (CHD) were potential risk factors for carotid tortuosity (p< 0.05). CONCLUSION: There was a significant correlation observed between the TIs of both the EICA and EVA and the presence of mild cognitive impairment. Advanced age, female, HP, DM, and CHD were independent risk factors for EICA and EVA tortuosities.
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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 | 0.001 |
| 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) | 0.002 | 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".