23 CAROTID ARTERY ULTRASOUND PROFILE AND COGNITIVE IMPAIRMENT IN CORONARY ARTERY DISEASE
Bibliographic record
Abstract
Background: Coronary artery disease is a common cardiovascular disease caused by atherosclerotic plaque accumulation in epicardial arteries. Atherosclerotic plaque, along with hypoperfusion and oxidative stress may cause cerebral dysfunction that leads to cognitive impairment. This study will further assess the correlation between cardiac function, carotid artery profile, and thus their impact on cognitive impairment. Methods: A cross-sectional study was conducted at Dr. Cipto Mangunkusumo Hospital, Jakarta between January 2020-June 2021. Inclusion criteria were coronary artery disease; exclusion criteria were history of stroke, brain injury, tumour, or infection. All subjects underwent carotid/transcranial doppler ultrasound and cognitive function examination (MOCA-Ina and MMSE) by certified neurologists. Normal ejection fraction defined as LVEF ≥55%, normal MOCA-Ina as score 26 and above, and normal MMSE as score 25 and above. Results: Twenty-nine subjects were enrolled in this study. Twenty-four (82.75%) subjects suffered from cognitive impairment where 19 (65.5%) subjects have abnormal MOCA-Ina or MMSE or both and 5 subjects (17.2%) subjects have abnormalities in at least one cognitive function domain. Memory function was the most common domain affected (79.17%), followed by executive function (58.3%), visuospatial (29.17%), attention (8.3%) and language (8.3%). However, the carotid artery ultrasound profile was within normal limits. Conclusion: Cognitive impairment is common disabling comorbidity found in coronary artery disease with memory function as the most frequent domain affected; although carotid artery profile was normal. Therefore, patients with coronary artery disease should be advised to undergo cognitive function screening and carotid ultrasound, especially those with lower ejection fractions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".