Effect of Carotid Artery Morphological Variations on Cognitive Function
Bibliographic record
Abstract
Background. Carotid artery morphological variations (CAMV) are common variations on medical imaging; the effects of CAMV on cognition were still unknown. This study is aimed at investigating whether carotid artery morphological variations (CAMV) cause cognitive impairment. Methods. Hospitalized patients from March 2017 to October 2017 who underwent digital subtract angiography (DSA) were divided into non-CAMV group, T-type group, K-type group, and C-type group according to their carotid artery morphology. Cognitive function in each group was evaluated with the Mini-Mental State Scale (MMSE), the Montreal Cognitive Assessment (MoCA), the Verbal Fluency Test (VFT), and the Digital Span Test (DST). Results. A total of 96 patients were included in the study (32 in non-CAMV group, 34 in T-type group, 30 in K-type group, and none in C-group). The positive rate of MMSE in the non-CAMV group, the T-type group, and the K-type group was 15.6%, 14.7%, and 20.0%, respectively, with no statistical difference in the three groups ( p=0.836 ). The positive rate of MoCA in the K-type group was significantly higher than that in the non-CAMV and the T-type groups ( p<0.001 ), but there was no significant difference between the non-CAMV group and the T-type group ( p=0.826 ). The VFT, DST forward score, and backward score in the K-type group were significantly lower than those in the non-CAMV and the T-type groups ( p<0.001 ). Conclusions. K-type CAMV may cause cognitive impairment, and MoCA is superior to MMSE in identifying mild cognitive impairment caused by CAMV.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".