Cecal intubation time between cap-assisted water exchange and water exchange colonoscopy: a randomized-controlled trial
Bibliographic record
Abstract
BACKGROUND AND AIM: The water exchange (WE) method can decrease the discomfort of the patients undergoing colonoscopy. It also provides salvage cleansing and improves adenoma detection, but a longer intubation time is required. Cap-assisted colonoscopy leads to a significant reduction in cecal intubation time compared with traditional colonoscopy with air insufflation. The aim of this study was to investigate whether combined cap-assisted colonoscopy and water exchange (CWE) could decrease the cecal intubation time compared with WE. PATIENTS AND METHODS: A total of 120 patients undergoing fully sedated colonoscopy at a regional hospital in southern Taiwan were randomized to colonoscopy with either CWE (n=59) or WE (n=61). The primary endpoint was cecal intubation time. RESULTS: The mean cecal intubation time was significantly shorter in CWE (12.0 min) compared with WE (14.8 min) (P=0.004). The volume of infused water during insertion was lower in CWE (840 ml) compared with WE (1044 ml) (P=0.003). The adenoma detection rate was 50.8 and 47.5% for CWE and WE, respectively (P=0.472). The Boston Bowel Preparation Scale scores were comparable in the two groups. Results from the multiple linear regression analysis indicated that WE with a cap, a higher degree of endoscopist's experience, a higher Boston Bowel Preparation Scale score, and a lower volume of water infused during insertion, without abdominal compression, without change of position, and without chronic laxative use, were significantly associated with a shorter cecal intubation time. CONCLUSION: In comparison with WE, CWE could shorten the cecal intubation time and required lower volume of water infusion during insertion without compromising the cleansing effect of WE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".