Electroacupuncture for postoperative recovery of gastrointestinal function in patients with gastrointestinal cancers: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective To evaluate the efficacy and safety of electroacupuncture (EA) for postoperative recovery in patients with gastrointestinal (GI) cancers. Methods We retrieved articles from PubMed, Embase, OVID, Cochrane Library, Web of Science, CINAHL, SinoMed, China National Knowledge Infrastructure (CNKI), Wanfang, and Technology Journal Database (VIP) from database inception to November 1 2024. Randomized controlled trials (RCTs) that examining the use of EA to improve GI function, reduce pain, and promote self-care ability after GI cancer surgery were included. Based on the type of control interventions, separate meta-analyses were conducted for EA vs postoperative nursing (PN) and EA vs sham acupuncture (SA). The primary outcomes were the time to first flatus (TFF) and the time to first defecation (TFD). The secondary outcomes included the time to recovery of bowel sounds (TRBS), the time to tolerance of liquid diet (TTLD), the time to tolerance of semiliquid diet (TTSD), the time to independent walking (TIW), the length of hospitalization (LH), and visual analog scale (VAS) immediate resting pain scores measured on the first, second and third postoperative days (POD 1-3). Results are reported as mean differences (MDs) with 95% confidence intervals (CIs). RevMan 5.3 was used for meta-analysis, StataSE 15.1 was used for sensitivity analyses and Egger’s tests. This study was registered on PROSPERO (CRD42022314754). Results A total of 19 RCTs involving 1902 participants were included, all of which were conducted in China between 2004 and 2023. When EA compared with PN, the meta-analysis showed EA significantly reduce TFF (n = 673, MD = -13.14, 95% CI = [-18.97 to -7.31], P < 0.00001), TFD (n = 598, MD = -19.86, 95% CI = [-27.83 to -11.89], P < 0.00001), TRBS (n = 216, MD = -12.44, 95% CI = [-15.00 to -9.87], P < 0.00001), TTLD (n = 268, MD = -18.14, 95% CI = [-24.98 to -11.29], P < 0.00001), TTSD (n = 141, MD = -20.44, 95% CI = [-33.84 to -7.04], P = 0.003), VAS on POD 1 (n = 299, MD = -0.52, 95% CI = [-0.92 to -0.11], P = 0.01), VAS on POD 2 (n = 256, MD = -0.91, 95% CI = [-1.23 to -0.60], P < 0.00001), VAS on POD 3 (n = 203, MD = -0.57, 95% CI = [-0.80 to -0.34], P < 0.00001), while no significantly decreasing in the LH (n = 322, MD = -1.16, 95% CI = [-2.56 to 0.24], P = 0.10). As EA compared with SA, EA could significantly reduce TFF (n = 782, MD = -15.78, 95% CI = [-24.96 to -6.60], P = 0.0008), TFD (n = 782, MD = -20.42, 95% CI = [-36.14 to -4.70], P = 0.01), LH (n = 782, MD = -1.37, 95% CI = [-2.69 to -0.05], P = 0.04), TIW (n = 743, MD = -0.33, 95% CI = [-0.62 to -0.04], P = 0.03).13 studies reported that EA reduced the incidence of postoperative complications, and 7 studies reported safety assessments of acupuncture-related adverse events, including hematoma, residual needling, sharp pain, pain, soreness or swelling after needle removal, with no serious adverse events. Conclusion EA can significantly promote the recovery of GI function, reduce postoperative pain and accelerate the improvement of self-care ability in patients undergoing surgery for GI cancers.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".