The Impact of Patient Education on the Quality of Inpatient Bowel Preparation for Colonoscopy
Bibliographic record
Abstract
BACKGROUND: For patients requiring colonoscopy while admitted to hospital, achieving adequate cleansing of the colon is often difficult. OBJECTIVES: To assess the impact of patient education, in the form of both counselling and written instructions, on bowel cleanliness at colonoscopy. METHODS: A total of 38 inpatients at a tertiary care hospital in Vancouver, British Columbia, who were referred to the gastroenterology service for colonoscopy were enrolled in the present study. Sixteen patients were randomly assigned to the intervention group, while 22 patients comprised the control group. Both groups received a clear liquid diet and 4 L of a commercially available bowel preparation. The intervention group also received a brief counselling session and written instructions outlining the methods and rationale for bowel preparation before colonoscopy. Bowel cleanliness was assessed by the endoscopist using a five-point rating scale. RESULTS: The two groups were similar with respect to demographics, the indication for colonoscopy and findings at colonoscopy. The median bowel cleanliness scores in the control group and the enhanced-instruction group were 3.0 and 2.0, respectively (P=0.001). CONCLUSION: Patient counselling and written instructions are inexpensive, safe and simple interventions. Such interventions are an effective means of optimizing colonoscopy preparation in the inpatient setting.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".