Effect of oral simethicone on the quality of colonoscopy: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: In this systematic review and meta-analysis, we aimed to investigate the effect of oral simethicone (SIM), an antifoaming agent, on the quality of colonoscopy in terms of bowel preparation quality, adenoma or polyp detection rate (ADR/PDR) and cecal intubation rate (CIR). METHODS: All randomized controlled trials (RCTs) on the use of SIM during bowel preparation for colonoscopy published up to 17 March 2021 were identified from the PubMed, EMBASE and Cochrane Library databases. Bowel preparation quality, ADR/PDR/CIR, cecal intubation time (CIT), withdrawal time (WT), patients' tolerability, acceptability and volume of foam and bubbles were compared between the SIM and non-SIM groups. RESULTS: Thirty-eight RCTs with 10 505 patients were included. Oral SIM significantly increased the rate of total Boston bowel preparation scale (BBPS) score ≥6 (risk ratio [RR] 1.13, P < 0.0001), acceptability (RR 1.15, P = 0.01) and the rate of no or minimal foam and bubbles (RR 1.28, P < 0.00001) and decreased abdominal distension (RR 0.64, P < 0.0001). However, it had no significant impact on overall ADR, overall PDR, CIR, CIT or WT. The rate of total BBPS score ≥6 remained significantly higher in the SIM group when a single-dose laxative regimen or a SIM dosage of ≥320 mg was employed; and ADR, PDR and CIR were significantly increased in the SIM group among colonoscopy clinicians who achieved an ADR <31%, PDR <45% and CIR <96%, respectively. CONCLUSIONS: Oral SIM can improve bowel preparation quality, especially in patients receiving a SIM dosage of ≥320 mg or a single-dose laxative regimen. SIM may be preferred by junior colonoscopy physicians/trainees with a lower ADR/PDR or CIR.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.006 | 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".