Experience with water-aided colonoscopy in a Canadian community population
Bibliographic record
Abstract
Purpose: Water-aided colonoscopy, immersion (WI) or exchange (We), minimizes insertion pain and reduces sedation. We improves adenoma detection rates (ADR). In a performance improvement program, we assessed water-aided methods in a busy community GI practice in c anada. Methods: Over a 13 month period patients were offered water-aided colonoscopy with an option to complete the examination with on-demand sedation. WI was used in the first 1-2 months and replaced exclusively with W e thereafter. procedural outcomes were compared to a continuous retrospective cohort of standard sedated air insufflation (AI) colonoscopy. Results: 200 (106M/94F) water-aided colonoscopies were completed. cecal intubation without air insufflation was achieved in 93%. sedation was not required in 95% of patients accepting on-demand sedation. Average insertion time was 15 min with an average withdrawal time of 6 min. 145 (87M/58F) water-aided colonoscopies, completed for screening or surveillance were compared to a cohort of 145 (77M/68F) AI colonoscopies in the immediate prior period. The overall ADR of water-aided colonoscopy was 30% (43/145) with a proximal ADR of 21%. Using AI the overall ADR was 27.6% (40/145) with a proximal ADR of 14.5% (21/145). In males, the proximal colon ADR was 25% in water-aided colonoscopies and 14.3% using AI (p=0.0397). Conclusions: Water-aided colonoscopy is feasible in a busy community practice with respectable cecal intubation rates. A large percentage of patients consenting to on-demand sedation completed without sedation. ADR in male subjects is consistent with similar prior reports of a significantly higher ADR in the proximal colon using W e when compared to AI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".