Unveiling the influence of lateralized carotid artery revascularization on cognitive function: a comparative analysis
Bibliographic record
Abstract
Objective To investigate and compare the changes in cognitive function after carotid artery revascularization surgery on different sides. Methods From April 2019 to April 2021, patients with ≥70% carotid artery stenosis who were treated with carotid endarterectomy (CEA) or carotid artery stenting (CAS) were recruited for this study. The Montreal Cognitive Assessment (MoCA) instrument was used to evaluate cognitive function preoperatively and at 3, 6, and 12 months postoperatively. Patients were divided into two groups based on the side of the surgery, repeated measures ANOVA was used for comparisons. Results A total of 89 patients who met the criteria were enrolled and completed 1-year follow-up. At 3, 6, and 12 months after carotid revascularization, the total MoCA score and delayed recall score were significantly improved compared with the baseline scores ( p < 0.05). In patients who underwent left-sided revascularization, verbal fluency showed improvement at 12 months compared to baseline levels ( p < 0.05). In patients who underwent right-sided revascularization, attention showed improvement at 6 months compared to baseline, and the improvement in delayed recall at 6 and 12 months was still evident compared to the 3-month assessment ( p < 0.05). Conclusion Carotid revascularization can improve cognitive function in patients, with differences in cognitive function changes observed between left and right carotid revascularization procedures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".