Feasibility of sedation on demand in Taiwan using water exchange and air insufflation: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND AND AIM: Completion of colonoscopy without sedation eliminates sedation cost and complications. Reported in the United States and Europe, on-demand sedation is not routine practice in Taiwan. Water exchange (WE), characterized by infusion and nearly complete removal of infused water during insertion, reduces insertion pain compared to air insufflation (AI) during colonoscopy. We evaluated the feasibility of on-demand sedation in Taiwan. In a randomized controlled trial of WE vs AI colonoscopy, we also aimed to determine if WE augmented the implementation by reducing insertion pain and decreasing sedation requirement. METHODS: This prospective patient-blinded study randomized patients to AI or WE (75 patients/group) to aid insertion. The primary outcome was the proportion of patients completing without sedation. RESULTS: In the AI and WE groups, 76.0% and 93.3% (P = 0.006) completed without need for sedation, respectively. The WE group had lower insertion pain score (mean [SD]) (4.0 [2.9] vs 2.1 [2.6], P < 0.001), lower doses of propofol (25.7 [52.7] mg vs 9.1 [35.6] mg, P = 0.012), and less time in the recovery room (3.4 [7.4] vs 1.5 [5.5], P = 0.027) than the AI group. Patient satisfaction scores and willingness to repeat if needed in the future were similar. CONCLUSION: On-demand sedation was feasible in Taiwan. The completion rate without sedation was high in patients (76.0% with standard AI) open to the option (no prior intent to receive the standard of full or minimal sedation). WE augmented the implementation by reducing insertion pain and decreasing sedation requirement without adversely affecting patient satisfaction or willingness to repeat.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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) | 0.005 | 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".