The Effects of Gum Chewing in the Postoperative‐Period: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: Postoperative ileus (POI) is a transient impairment of gastrointestinal motility occurring after surgery. Patients with POI can suffer from complications such as increased risk of nosocomial infections, overall mortality risk, and higher overall healthcare costs. This study aims to (a) evaluate the effectiveness of gum chewing on relieving POI symptoms (b) conduct meta-analysis on POI outcome variables to estimate overall effect size. METHODS: We searched MEDLINE (Ovid), EMBASE, CINAHL, Web of Science, and Scopus for English language randomized controlled trials (RCTs), evaluating the postoperative effects of gum chewing in adult surgical patients. Two reviewers independently screened titles, abstracts, and full texts, resolving conflicts by discussion. Study quality was assessed using the GRADE system. Meta-analysis was conducted within two surgical specialty subgroups separately. RESULTS: From 1925 citations, 714 duplicates were removed. After title/abstract screening, 128 proceeded to full text review, out of which 43 studies met the inclusion criteria. Among RCTs, 69.8% noted statistically significant improvement in at least one of the POI outcomes in the intervention cohort. Meta-analysis showed significant reductions in time to first flatus (-7.4 h; p = 0.0003) and defecation (-18.64 h; p < 0.0001) in general and colorectal surgery. Similar reductions were also observed in the OBGYN and gynecology cohort: flatus (-6.52 h; p < 0.0001) and defecation (-9.71 h; p = 0.003). CONCLUSION: Chewing gum is a safe low-cost intervention that has the potential to significantly reduce POI symptoms and hospitalization times. Meta-analysis validated the clinical relevance of the intervention. Standardized protocols are still required to delineate true effect sizes and reduce heterogeneity.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| 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) | 0.004 | 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".