Unravelling the analgesic effects of perioperative magnesium in general abdominal surgery: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Prior research has established the effectiveness of magnesium in relieving postoperative pain. This article aims to evaluate magnesium sulfate for perioperative analgesia in adults undergoing general abdominal surgery under general anesthesia. OBJECTIVE: The primary aim was to assess pain scores at 6 and 24 hours postoperatively in patients receiving magnesium sulfate vs. the control group. Secondary outcomes were postoperative opioid consumption, perioperative complications, and time to rescue analgesia. METHODS: A comprehensive database search identified studies comparing magnesium sulfate with control in adults undergoing general anesthesia for general abdominal surgery. Using random-effects models, data were presented as mean ± Standard Deviation (SD) or Odds Ratios (OR) with corresponding 95% Confidence Intervals (95% CI). A two-sided p-value < 0.05 was considered statistically significant. RESULTS: In total, 31 studies involving 1762 participants met the inclusion criteria. The magnesium group showed significantly lower postoperative pain scores at both early (within six hours) and late (up to 24 hours) time points compared to the control group. The early mean score was 3.1 ± 1.4 vs. 4.2 ± 2.3, and the late mean score was 2.3 ± 1.1 vs. 2.7 ± 1.5, resulting in an overall Mean Difference (MD) of -0.72; 95% CI -0.99, -0.44; p < 0.00001. The magnesium group was associated with lower rates of postoperative opioid consumption and shivering and had a longer time to first analgesia administration compared to the saline control group. CONCLUSION: Magnesium sulfate administration was linked to reduced postoperative pain and opioid consumption following general abdominal surgery.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 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".