Edukacinio 3D virtualiosios realybės vaizdo metodo įtaka ruošiant žarnyną kolonoskopijai: pirmieji rezultatai
Bibliographic record
Abstract
Objective. Adequate bowel preparation is essential for diagnostic, screening, surveillance, and therapeutic colonoscopy. 3D virtual reality (3D-VR) has the characteristics of depth, interactivity and visuality and is widely used in medicine, so it can be used for patient education and training. The aim of our study is to determine the impact of using 3D virtual reality video for patients education on bowel preparation before colonoscopy. Materials and methods. A prospective, blind, randomized clinical trial was launched at the Republican Vilnius University Hospital (RVUL) on 07.03.2021, which included 50 outpatients who had indications for colonoscopy until 28.02.2022. Patients were randomly assigned to control and experimental groups. The first group was given the bowel preparation information in the standard form used by RVUL – in writing, and the second – in a 3D virtual reality video. The content of the information provided to both groups was the same. The quality of bowel preparation was assessed based on the Boston and Ottawa bowel preparation scales. Results. Of the 50 outpatients who participated in the study, 26 were assigned to the control group, 24 to the study group. The patients of both groups were identical in terms of sex, age, body mass index, comorbidities. The mean (SD) BBPS score was statistically significantly lower in the control group compared to the 3D-VR video group (5.96(±1) vs. 7.58(±1.47); p < 0.001). The mean (SD) scores of OBPS were higher in the control group (6.58(±2.44) than in the study group 1.83(±2.32); p < 0.001). The rate of adequate bowel preparation in the 3D-VR video group was higher than in the control group (18(69.23%) vs. 23(95.83%); the difference was statistically significant (p = 0.024)). The rate of terminal ileum intubation in the control group was 50% compared to 83.33% in the 3D-VR video group (p = 0.02).The mean (SD) colonoscopy time was statistically significantly shorter in the 3D-VR video group 23.04(±3.66) minutes and 16.5(±4.28) minutes, p = <0.001. Conclusions. Patients who were informed by 3D-VR method before colonoscopy had statistically significantly better bowel preparation, as well as reduced procedure time and possibly increased detection rates of polyps and adenomas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".