Impact of Bowel Preparation Type on Colonoscopy Quality and Adenoma Detection: A Comparative Study
Bibliographic record
Abstract
Background Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Colonoscopy is the gold standard for CRC screening, but its effectiveness depends on bowel preparation quality. This study compares polyethylene glycol (PEG)-based MoviPrep (Norgine Limited, Hengoed, UK) and sodium picosulfate-based Picolax (Ferring GmbH, Kiel, Germany) in terms of bowel cleansing quality, caecal and ileal intubation rates, and adenoma detection rate (ADR). Methods This retrospective observational study analysed 6,921 colonoscopies performed at University Hospital Crosshouse between June 2020 and June 2023. Bowel preparation quality was assessed using the modified Ottawa Bowel Preparation Scale, categorised as Excellent, Good, Fair, or Inadequate. ADR was determined by histologically confirmed adenomas. Statistical comparisons between the two groups were performed using chi-square tests. Results MoviPrep was used in 6,219 (89.9%) of cases, while Picolax was used in 702 (10.1%) cases. MoviPrep was associated with a lower inadequate preparation rate (343 (5.5%) vs. 63 (9.0%)), a higher caecal intubation rate (3,675 (59.1%) vs. 307 (43.7%)) and ileal intubation rate (1,119 (18.0%) vs. 81 (11.5%)), and a higher ADR (2,295 (36.9%) vs. 167 (23.8%)). Conclusion MoviPrep demonstrated superior bowel cleansing, higher completion rates, and greater adenoma detection, supporting its use as a preferred bowel preparation method for colonoscopy in clinical practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".