Effect of Perioperative Electroacupuncture as An Adjunctive Therapy on Postoperative Analgesia with Tramadol and Ketamine in Prostatectomy: A Randomised Sham-Controlled Single-Blind Trial
Bibliographic record
Abstract
OBJECTIVES: To study the analgesic effect of electroacupuncture (EA) as perioperative adjunctive therapy added to a systemic analgesic strategy (including tramadol and ketamine) for postoperative pain, opioid-related side effects and patient satisfaction. METHODS: In a sham-controlled participant- and observer-blinded trial, 75 patients undergoing radical prostatectomy were randomly assigned to two groups: (1) EA (n=37; tramadol+ketamine+EA) and (2) control (n=38; tramadol+ketamine). EA (100 Hz frequency) was applied at LI4 bilaterally during the closure of the abdominal walls and EA (4 Hz) was applied at ST36 and LI4 bilaterally immediately after extubation. The control group had sham acupuncture without penetration or stimulation. The following outcomes were evaluated: postoperative pain using the Numerical Rating Scale (NRS) and McGill Scale (SF_MPQ), mechanical pain thresholds using algometer application close to the wound, cortisol measurements, rescue analgesia, Spielberger State Trait Anxiety Inventory (STAI Y-6 item), patient satisfaction and opioid side effects. RESULTS: Pain scores on the NRS and SF_MPQ were significantly lower and electronic pressure algometer measurements were significantly higher in the EA group than in the control group (p<0.001) at all assessments. In the EA group a significant decrease in rescue analgesia was observed at 45 min (p<0.001) and a significant decrease in cortisol levels was also observed (p<0.05). Patients expressed satisfaction with the analgesia, especially in the EA group (p<0.01). Significant delays in the start of bowel movements were observed in the control group at 45 min (p<0.001) and 2 h (p<0.05). CONCLUSIONS: Adding EA perioperatively should be considered an option as part of a multimodal analgesic strategy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".