Postoperative Gastrointestinal Dysfunction After Neuromuscular Blockade Reversal With Sugammadex Versus Cholinesterase Inhibitors in Patients Undergoing Gastrointestinal Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Postoperative gastrointestinal dysfunction (POGD) commonly occurs following gastrointestinal (GI) surgery and is associated with specific anesthetic agents. Cholinesterase inhibitors employed for reversing neuromuscular blockade have been implicated in development of POGD. Sugammadex, a novel reversal agent, is linked with reduced POGD. However, there is a lack of comprehensive comparative review between these agents regarding their impact on POGD following GI surgery. This study aims to systematically review the effects of sugammadex on POGD compared to cholinesterase inhibitors following GI surgery. METHODS: MEDLINE, EMBASE, and CENTRAL were searched as of July 2022 to identify articles comparing sugammadex with cholinesterase inhibitors in patients undergoing gastrointestinal surgery, specifically in relation to POGD. Secondary endpoints included length of hospital stay, readmission rates, pulmonary complications, and postoperative morbidity. RESULTS: = 56%, low certainty evidence) was observed with sugammadex. No significant difference in any other outcome was observed. Narrative review of readmission data demonstrated no significant difference. CONCLUSION: The use of sugammadex following gastrointestinal surgery is associated with significantly lower rates of prolonged postoperative ileus compared to cholinesterase inhibitors. However, these do not translate into a significant reduction in length of stay, morbidity, or postoperative nausea and vomiting. Results are limited by the numer of studies included and missing data, more robust RCTs are needed before recommendations can be made.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| 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".