The Impact of a Smartphone App on the Quality of Pediatric Colonoscopy Preparations: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Smartphone apps have been successfully used to help adults prepare for colonoscopies. However, no study to date has investigated the effect of a smartphone app on pediatric colonoscopy preparation. OBJECTIVE: The aim of this study is to determine if an app (SB Colonoscopy Prep) designed to educate and guide patients through their colonoscopy preparation will yield benefits over paper-based instructions and information. METHODS: In total, 46 patients aged 5-18 years received either app-based or written material with instructions on how to take their prep medications as well as information about the colonoscopy procedure. Prep quality, the number of calls to the gastroenterology service, and patient arrival time were recorded. After the procedure, a questionnaire was given to each patient through which they graded their knowledge of the procedure both before and after receiving the app or written material. RESULTS: App users had higher mean Boston scores versus control subjects receiving written instructions (7.2 vs 5.9, P=.02), indicating better colonoscopy preps. In total, 75% (15/20) of app users and 41% (9/22) of written instruction users had preps categorized as "excellent" on the Boston scale. We found no significant differences in knowledge about the procedure (app users: 10/20 [50%], written instruction users 8/22 [36%]; P=.37), phone calls to the gastroenterology clinic (n=6 vs n=2; P=.27), or arrival times at the endoscopy suite (44 min vs 46 min before the scheduled procedure time; P=.56). CONCLUSIONS: Smartphone app use was associated with an increased number of colonoscopy preps classified as "excellent" on the Boston scale. There was no significant difference between app users and the control group regarding the number of calls to the gastroenterology clinic, patient arrival time, or patient knowledge about the procedure. TRIAL REGISTRATION: ClinicalTrials.gov NCT04590105; https://clinicaltrials.gov/ct2/show/NCT04590105.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".