Smartphone Application Versus Standard Instruction for Colonoscopic Preparation
Bibliographic record
Abstract
OBJECTIVE: To compare smartphone application (Colonoscopic Preparation) instructions versus paper instructions for bowel preparation for colonoscopy. BACKGROUND: Adhering to bowel preparation instructions is important to ensure a high-quality colonoscopy. PATIENTS AND METHODS: This randomized controlled trial included individuals undergoing colonoscopy at a tertiary care hospital. Individuals were randomized (1:1) to receive instructions through a smartphone application or traditional paper instructions. The primary outcome was the quality of the bowel preparation as measured by the Boston Bowel Preparation Score. Secondary outcomes included cecal intubation and polyp detection. Patient satisfaction was assessed using a previously developed questionnaire. RESULTS: A total of 238 individuals were randomized (n = 119 in each group), with 202 available for the intention-to-treat analysis (N = 97 in the app group and 105 in the paper group). The groups had similar demographics, indications for colonoscopy, and type of bowel preparation. The primary outcome (Boston Bowel Preparation Score) demonstrated no difference between groups (Colonoscopic Preparation app mean: 7.26 vs paper mean: 7.28, P = 0.91). There was no difference in cecal intubation ( P = 0.37), at least one polyp detected ( P = 0.43), or the mean number of polyps removed ( P = 0.11). A higher proportion strongly agreed or agreed that they would use the smartphone app compared with paper instructions (89.4% vs 70.1%, P = 0.001). CONCLUSIONS: Smartphone instructions performed similarly to traditional paper instructions for those willing to use the application. Local patient preferences need to be considered before making changes in the method of delivery of medical instructions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".