Educational virtual reality videos in improving bowel preparation quality and satisfaction of outpatients undergoing colonoscopy: protocol of a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Colonoscopy is the reference method in screening and diagnosis of colorectal neoplasm, but its efficacy is closely related to the quality of bowel preparation. Poor patient compliance is a major risk factor for inadequate bowel preparation likely due to poor patient education. Such an education is usually provided via either oral or written instructions by clinicians. However, multiple education methods, such as smartphone applications, have been proved useful in aiding patients through bowel preparation. Also, it was reported that a large proportion of patients feel anxious before colonoscopy. Virtual reality (VR) is a novel method to educate patients and provides them with an immersive experience. Theoretically, it can make patients better prepared for bowel preparation and colonoscopy. However, no prospective studies have assessed the role of this novel technology in patient education before colonoscopy. We hypothesise that VR videos can improve bowel preparation quality and reduce pre-procedure anxiety. METHODS/DESIGN: The trial is a prospective, randomised, single-blinded, single-centre trial. Outpatients who are scheduled to undergo colonoscopy for screening or diagnostic purposes for the first time will be randomised to receive either the conventional patient education or the conventional methods plus VR videos, and 322 patients will be enrolled from the Peking Union Medical College Hospital. The primary endpoint is the quality of bowel preparation, measured by the Boston bowel preparation score. Secondary endpoints include polyp detection rate, adenoma detection rate, cecal intubation rate, patient compliance to complete bowel cleansing, withdrawal time, pre-procedure anxiety, overall satisfaction and willingness for the next colonoscopy. ETHICS AND DISSEMINATION: The study has been approved by the institutional review board of the Peking Union Medical College Hospital (No. ZS-1647). The results of this trial will be published in an open-access way and disseminated among gastrointestinal physicians and endoscopists. TRIAL REGISTRATION NUMBER: NCT03667911.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".