Abstract TP569: The Evaluation of Cognitive Function Using Neural Network Analysis Before & After Revascularization Surgery for Internal Carotid Artery Stenosis
Bibliographic record
Abstract
Background: Internal carotid artery stenosis (ICS) can lead to cognitive impairment as well as ischemic stroke. Although carotid revascularization surgery, such as carotid endarterectomy (CEA) and carotid artery stenting (CAS), can prevent future strokes, the effect of revascularization on cognitive function is controversial. In recent years, the analysis of functional connectivity (FC) in resting-state functional MRI (rs-fMRI) has been used to investigate the effects of cognitive interventions. In this study, cognitive function is evaluated in ICS patients undergoing revascularization surgery with rs-fMRI. Methods: A prospective study was conducted among 17 ICS patients who were expecting revascularization surgery. Cognitive assessment, including the Mini-Mental State Examination (MMSE), the Frontal Assessement Battery (FAB), and the Japanese version of the Montreal Cognitive Assessment (MoCA-J) and rs-fMRI were administered ≤ 1 week preoperatively and postoperatively at 3 months. For the analysis of FC, a seed region was placed in the posterior cingulate cortex (PCC) associated with cognitive function. Results: After revascularization surgery, significant improvement in the score of MMSE (28.1 vs 29.1, P = 0.01) and MoCA-J (24.0 vs 26.7, P = 0.001) was found. No significant difference was found in the score of the FAB (16.2 vs 16.8, P = 0.09) between before and after surgery. According to the analysis of FC, ICS patients showed increase of connectivity between PCC and posterior cingulate gyrus, and between PCC and precuneus postoperatively at 3 months. Conclusion: Revascularization surgery for ICS improves cognitive function. The cognitive improvement may be partly attributed to the increase of connectivity between PCC and posterior cingulate gyrus/precuneus.
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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 | 0.001 |
| 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) | 0.002 | 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".