Efficacy of Prokinetics with a Split-Dose of Polyethylene Glycol in Bowel Preparation for Morning Colonoscopy: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND/AIMS: Currently, a split-dose of polyethylene glycol (PEG) is the mainstay of bowel preparation due to its tolerability, bowel-cleansing action, and safety. However, bowel preparation with PEG is suboptimal because residual fluid reduces the polyp detection rate and requires a more thorough colon inspection. The aim of our study was to demonstrate the efficacy of a sufficient dose of prokinetics on bowel cleansing together with split-dose PEG. METHODS: A prospective endoscopist-blinded study was conducted. Patients were randomly allocated to two groups: prokinetic with split-dose PEG or split-dose PEG alone. A prokinetic [100 mg itopride (Itomed)], was administered twice simultaneously with each split-dose of PEG. Bowel-cleansing efficacy was measured by endoscopists using the Ottawa scale and the segmental fluidity scale score. Each participant completed a bowel preparation survey. Mean scores from the Ottawa scale, segmental fluid scale, and rate of poor preparation were compared between both groups. RESULTS: Patients in the prokinetics with split-dose PEG group showed significantly lower total Ottawa and segmental fluid scores compared with patients in the split-dose of PEG alone group. CONCLUSION: A sufficient dose of prokinetics with a split-dose of PEG showed efficacy in bowel cleansing for morning colonoscopy, largely due to the reduction in colonic fluid.
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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) | 0.004 | 0.002 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".