Improving the Quality of Colonoscopy Bowel Preparation Using an Educational Video
Bibliographic record
Abstract
Colonoscopy is the preferred modality for colon cancer screening. A successful colonoscopy requires proper bowel preparation. Adequate bowel preparation continues to remain a limiting factor. One hundred thirty-three patients scheduled for an outpatient colonoscopy were prospectively randomized in a single-blinded manner to video or nonvideo group. In addition to written bowel preparation instructions, patients in the video group viewed a brief instructional video. Quality of colon preparation was measured using the Ottawa Bowel Preparation Quality scale, while patient satisfaction with preparation was evaluated using a questionnaire. Statistical analyses were used to evaluate the impact of the instructional colonoscopy video. There were significant differences in the quality of colonoscopy preparation between the video and the nonvideo groups. Participants who watched the video had better preparation scores in the right colon (P=0.0029), mid-colon (P=0.0027), rectosigmoid (P=0.0008), fluid content (P=0.03) and aggregate score (median score 4 versus 5; P=0.0002). There was no difference between the two groups with regard to patient satisfaction. Income, education level, sex, age and family history of colon cancer had no impact on quality of colonoscopy preparation or patient satisfaction. The addition of an instructional bowel preparation video significantly improved the quality of colon preparation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".