Electroacupuncture for the Prevention of Perioperative Neurocognitive Disorder in Older Patients Undergoing General Anesthesia: Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Perioperative neurocognitive disorder (PND) is a prevalent complication among older patients undergoing general anesthesia, imposing significant burdens on individuals, health care systems, and society. While electroacupuncture shows promise for PND prevention, current evidence remains inconclusive. Objective: This study aims to critically evaluate the effectiveness and safety of perioperative electroacupuncture for PND prevention in older patients undergoing surgery under general anesthesia. Methods: A comprehensive literature search will be conducted in 8 electronic databases (PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Chongqing VIP Chinese Science and Technology Periodical Database, Wan Fang Database, and China Biomedical Literature Database) and 3 clinical trial registries from inception to March 16, 2025. The search strategy aims to identify all relevant randomized controlled trials evaluating perioperative electroacupuncture for PND prevention in older patients (aged ≥60 years) undergoing general anesthesia. The primary outcome will be the incidence of PND. Secondary outcomes will include (1) neuropsychological assessment scores (Mini-Mental State Examination and Montreal Cognitive Assessment), (2) serum inflammatory biomarker levels (interleukin-1β, interleukin-6, and tumor necrosis factor-α), (3) serum neurological damage marker levels (neuron-specific enolase and S100 calcium-binding protein β), and (4) safety outcomes (incidence of adverse events). Two independent reviewers will perform study selection, data extraction, and methodological quality assessment using the revised Cochrane risk of bias tool for randomized trials. All statistical analyses will be conducted in RevMan 5.4 using suitable meta-analysis models based on heterogeneity testing. The certainty of evidence will be evaluated using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Results: The study selection process will be presented through a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram, detailing the number of records identified, screened, and included. Characteristics of eligible studies will be summarized in evidence tables, including study designs and populations, intervention protocols, and outcome measures. The results will be visualized through a risk of bias graph, forest plots displaying pooled effect estimates with 95% CIs, and funnel plots for publication bias evaluation (when ≥10 studies are available). This protocol is currently in the active phase. The literature search has been completed as of April 2025, with an updated search planned until December 31, 2025. Data extraction is scheduled to commence on January 15, 2026, followed by data analysis starting February 1, 2026. Results are expected to be submitted for publication in March 2026. Conclusions: The effectiveness and safety of perioperative electroacupuncture for PND prevention in older patients undergoing general anesthesia remain uncertain. This systematic review will provide an evidence-based evaluation of perioperative electroacupuncture's effectiveness in preventing PND, offer practical recommendations for optimizing surgical care for older adults, and identify knowledge gaps to inform future research.
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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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".