Higher Grade Glioma Increases the Risk of Postoperative Delirium: Deficient Brain Compensation Might Be a Potential Mechanism of Postoperative Delirium
Bibliographic record
Abstract
Objective The brain compensation mechanism in postoperative delirium (POD) has not been reported. We uncovered the mechanism by exploring the association between POD and glioma grades, and the relationship between preoperative brain structural and functional compensation with POD in patients with frontal glioma. Methods A total of 335 adult patients with glioma were included. The multivariable analysis examined the association between tumor grade and POD. Then, 20 patients with left frontal lobe glioma who had presurgical structural and functional MRI data and Montreal Cognitive Assessment (MoCA) in this cohort were analyzed. We measured the gray matter volume (GMV) and functional connectivity (FC) in patients with (n = 8) and without (n = 12) POD and healthy controls (HCs, n = 29) to detect the correlation between the structural and functional alteration and POD. Results The incidence of POD was 37.3%. Multivariable regression revealed that high-grade glioma had approximately six times the odds of POD. Neuroimaging data showed that compared with HC, the patients with left frontal lobe glioma showed significantly increased GMV of the right dorsal lateral prefrontal cortex (DLPFC) in the non-POD group and decreased GMV of right DLPFC in the POD group, and the POD group exhibited significantly decreased FC of right DLPFC, and the non-POD group showed the increasing tendency. Partial correlation analysis showed that GMV in contralesional DLPFC were positively correlated with preoperative neurocognition, and the GMV and FC in contralesional DLPFC were negatively correlated with POD. Conclusions Our findings suggested that insufficient compensation for injured brain regions involving cognition might be more vulnerable to suffering from POD.
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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 | 0.001 |
| 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 | 0.000 |
| 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".