The predictive power of hemodynamic data on postoperative neurocognitive impairment: a logistic regression and random forest approach
Bibliographic record
Abstract
This study assesses hemodynamic data and parameter combinations in predicting neurocognitive impairment post-cardiopulmonary bypass graft (CABG) using logistic regression and random forest algorithms. 28 patients underwent the Montreal Cognitive Assessment (MoCA) test preoperatively and one month postoperatively. Patients were grouped by MoCA score changes: Group 1 (< 2 points decrease) and Group 2 (≥ 2 points decrease). Real-time hemodynamic data were recorded during surgery, and after artifact removal, a large dataset was analyzed. Derived parameters included Absolute Maximum Decrease (AMD), areas under thresholds, and duration spent below thresholds. Logistic regression and Random Forest algorithms assessed individual and combined parameter effects. Partial Dependence Plots (PDPs) aided interpretability. Results: Logistic regression and Random Forest analyses indicated hemodynamic data have limited predictive power for neurocognitive impairment. No logistic regression analysis yielded statistically significant results, and no Random Forest model achieved high accuracy. Conclusion: Hemodynamic data alone are insufficient for prediction. Including cerebral oxygen saturation, micro emboli, and hematocrit may improve model performance. Larger sample sizes and long-term follow-up are recommended for better accuracy. This study provides a basis for future research to mitigate postoperative cognitive dysfunction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".