A survey of colonoscopists with and without in‐depth knowledge of water‐aided colonoscopy
Bibliographic record
Abstract
BACKGROUND AND AIM: Endoscopy featured water-aided colonoscopy (WAC) as novel in the Innovation Forum in 2011. Gastrointestinal Endoscopy published a modified Delphi consensus review (MDCR) that supports WAC for clinical practice in 2021. We tested the hypothesis that experience was an important predictor of WAC use, either as water immersion (WI), water exchange (WE), or a combination of WI and WE. METHODS: A questionnaire was sent by email to the MDCR authors with an in-depth knowledge of WAC. They responded and also invited colleagues and trainees without in-depth knowledge to respond. Logistic regression analysis was used with the reasons for WAC use treated as the primary outcome. Reports related to WAC post MDCR were identified. RESULTS: Of 100 respondents, > 80% indicated willingness to adopt and modify practice to accommodate WAC. Higher adenoma detection rate (ADR) incentivized WE use. Procedure time slots ≤ 30 and > 30 min significantly predicted WI and WE use, respectively. Co-authors of the MDCR were significantly more likely to perform WAC (odds ratio [OR] = 7.5, P = 0.037). Unfamiliarity with (OR = 0.11, P = 0.02) and absence of good experience (OR = 0.019, P = 0.002) were associated with colonoscopists less likely to perform WAC. Reports related to WAC post MDCR revealed overall and right colon WE outcomes continued to improve. Network meta-analyses showed that WE was superior to Cap and Endocuff. On-demand sedation with WE shortened nursing recovery time. CONCLUSIONS: An important predictor of WAC use was experience. Superior outcomes continued to be reported with WE.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".