Cerebrovascular dysregulation and postoperative cognitive alterations after carotid endarterectomy
Bibliographic record
Abstract
Abstract There are controversial data about the effect of carotid endarterectomy regarding postoperative cognitive function. Our aim was to analyze the effect of cerebral tissue saturation monitored by near-infrared spectroscopy (NIRS) on cognitive function. Perioperative data of 103 asymptomatic patients undergoing elective carotid surgery under general anesthesia were analyzed. Preoperatively and 3 months after the operation, MMSE (Mini Mental State Examination) and MoCA (Montreal Cognitive Assessment) tests were conducted. For cerebral monitoring, NIRS was used, and the lowest rSO2 value and the degree of desaturation were calculated. Cognitive changes were defined as one standard deviation change from the preoperative test scores, defined as postoperative neurocognitive decline (PNCD) and cognitive improvement (POCI). PNCD was found in 37 patients (35.92%), and POCI was found in 18 patients (17.47%). Female gender, patients with diabetes, and the degree of desaturation were independently associated with PNCD. The degree of desaturation during the cross-clamp period negatively correlated with the change in the MoCA scores (R = − 0.707, p = 0.001). The 15.5% desaturation ratio had 86.5% sensitivity and 78.8% specificity for discrimination. For POCI, a desaturation of less than 12.65% had 72.2% sensitivity and 67.1% specificity. POCI was associated with lower preoperative MOCA scores and a lower degree of desaturation. We found a significant relation between the change of postoperative cognitive function proven by the MoCA test and cerebral tissue saturation during the clamping period in patients undergoing carotid endarterectomy.
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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 | 0.000 | 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) | 0.001 | 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".