Pharmacological Agents That Prevent Postoperative Cognitive Dysfunction in Patients With General Anesthesia: A Network Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Postoperative cognitive dysfunction (POCD) is associated with prolonged hospital stays, increased mortality, and negative socioeconomic consequences. Dexmedetomidine, ketamine, dexamethasone, and lidocaine have previously been reported to be effective for preventing POCD. STUDY QUESTION: In this network meta-analysis, we apply direct and indirect comparisons to rank these pharmacological agents in terms of their effect on POCD, through which we seek to provide evidence for future clinical medication. DATA SOURCES: A comprehensive literature search of PubMed, EMBASE, the Cochrane Library, and Web of Science was conducted to identify randomized controlled trials that examined the effects of dexmedetomidine, ketamine, dexamethasone, or lidocaine on POCD induced by general anesthesia. STUDY DESIGN: For eligible studies, 2 reviewers independently extracted data and assessed the respective risk of bias. Bayesian network meta-analysis was conducted using R-3.4.1 software. A total of 30 articles were included in this meta-analysis. RESULTS: Direct comparison showed that dexmedetomidine [odds ratio (OR) = 0.42, 95% confidence interval (CI): 0.32-0.55] may decrease the incidence of POCD for noncardiac surgery; dexmedetomidine (OR = 0.08, 95% CI: 0.01-0.63) and ketamine (OR = 0.09, 95% CI: 0.02-0.32) were found to decrease POCD for cardiac surgery. The R-software ranking result for prevention of POCD was dexmedetomidine, lidocaine, ketamine, placebo, and dexamethasone, respectively, in noncardiac surgery, and dexmedetomidine, ketamine, lidocaine, placebo, and dexamethasone in cardiac surgery. CONCLUSION: Dexmedetomidine exhibited obvious superiority to other agents for noncardiac surgery; dexmedetomidine and ketamine exhibited a significantly better performance than other agents for cardiac surgery. Dexamethasone did not show better efficacy than the placebo. However, more rigorously designed studies comprising larger sample sizes are needed to confirm our findings.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.056 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".