Predictors of Early Neurocognitive Deficits in Low-Risk Patients Undergoing On-Pump Coronary Artery Bypass Surgery
Bibliographic record
Abstract
BACKGROUND: Postoperative cognitive deficits (POCDs) are a source of morbidity and occur frequently even in low-risk patients undergoing cardiac surgery. Predictors of neurocognitive deficits can identify potentially modifiable risk factors as well as high-risk patients in whom alternate revascularization strategies may be considered. METHODS AND RESULTS: 448 patients undergoing coronary surgery (coronary artery bypass graft [CABG]) underwent standardized preoperative and postoperative neurocognitive testing as part of 2 randomized trials evaluating the effects of mild hypothermia during coronary surgery. Prospectively collected data were used to identify univariate predictors of POCDs and multivariable logistic regression models were constructed. Models were bootstrapped 1000 times. POCDs occurred in 59% of patients. Significant univariate predictors included intraoperative normothermia, impaired left ventricular (LV) function, higher educational level, elevated serum creatinine and reduced creatinine clearance, prolonged intubation time, intensive care unit (ICU) stay, and hospital stay. Advanced age, presence of carotid disease, and cardiopulmonary bypass time were not associated with increased POCDs in this cohort. Multivariable modeling identified intraoperative normothermia (odds ratio [95% confidence interval] -1.15 [1.01, 1.31]), poor LV function (1.53 [1.02, 2.30]), and elevated preoperative creatinine (1.01 [1.00 to 1.03] for every 1 mmol/L increase), prolonged (>24 hours) ICU stay (1.88 [1.27 to 2.79]), and higher educational level (1.52 [1.01 to 2.28]) as independent predictors of POCD occurrence. CONCLUSIONS: Mild hypothermia, in the intraoperative and perioperative period, may be a protective strategy for the prevention of POCDs. Patients with elevated pre-operative creatinine and poor LV function carry a higher risk of POCDs and may benefit from revascularization strategies other than conventional on-pump CABG.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".