Are Standard Follow-Up Parameters Sufficient to Protect Neurocognitive Functions in Patients with Diabetes Mellitus who Underwent Coronary Artery Bypass Grafting?
Bibliographic record
Abstract
OBJECTIVE: We aimed to compare the effectiveness of cognitive function protection between the standard follow-up parameters and advanced neuromonitoring methods in diabetic patients who underwent coronary artery bypass grafting during cardiopulmonary bypass. METHODS: Study design was prospective and observational. Patients were separated into two groups, treated only with standard follow-up parameters (Group 1) and followed up with the change of regional cerebral tissue oxygenation (rSO2) by near-infrared spectroscopy (Group 2). Neurocognitive functions were evaluated preoperatively and postoperatively before discharge in all patients using the Montreal Cognitive Assessment (MoCA) test. RESULTS: Cognitive functions of Group 2 patients in the postoperative period were significantly higher than Group 1 patients (P=0.001). The mean postoperative MoCA score of patients was significantly lower than the mean preoperative MoCA score in Group 1 (24.8±2.2 vs. 23.6±2.6, P=0.02). However, mild cognitive dysfunction was significantly lower in Group 2, compared to Group 1 (P=0.02). CONCLUSION: In patients followed up with standard parameters, a significant decrease in cognitive function was observed in the early period. However, the use of advanced neuromonitoring methods can significantly prevent this decrease in cognitive functions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".