Preoperative cognitive function as a risk factor of postoperative delirium in cancer surgeries: A systematic review and meta‐analysis
Bibliographic record
Abstract
Postoperative delirium (POD) after cancer surgeries can be a result of chemo brain, anesthesia, surgery duration, and preoperative cognitive impairment. Although older age and preoperative cognitive dysfunction were reported to increase the risk of POD in noncardiac surgery, the role of preoperative cognitive function and age in the development of POD after all types of cancer surgeries is not clear. This study aimed to determine the relationship between preoperative cognitive function and likelihood of POD after cancer surgeries. This study used three main online databases and followed PRISMA guidelines. English language original articles that examined preoperative cognitive function before solid tumor cancer surgery and assessed patients for postoperative delirium were included. We employed the random effect meta-analysis method. The overall incidence of POD ranged from 8.7% to 50.9%. The confusion assessment method was the most common tool used to assess delirium. Mini-mental state evaluation (MMSE), Mini-cog, and Montreal cognitive assessment were the most common tools to assess cognitive function. The pooled (total observation = 4676) random effects SMD was estimated at -0.84 (95% confidence interval [CI]: -1.30 to -0.31), indicating that lower MMSE scores before surgery are associated with a higher risk of POD. The pooled (total observation = 2668) random effects OR was estimated at 5.17 (95% CI: 2.51 to -10.63), indicating preoperative cognitive dysfunction can significantly predict the occurrence of POD after cancer surgeries. In conclusion, preoperative cognitive function is an independent and significant predictor of POD after solid tumor cancer surgeries.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".