A Customized Mobile Application in Colonoscopy Preparation: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: Adherence with diet and prescribed purgative is essential for proper cleansing with low-volume bowel preparations. The aim of this work was to assess the effect of a customized mobile application (App) on adherence and quality of bowel preparation. METHODS: One hundred and sixty (160) eligible patients scheduled for elective colonoscopy were randomly assigned to paper (control) or App-based instructions. The preparation consisted of low-fiber diet for 2 days, clear fluids for one day and split-dose sodium picosulfate/magnesium citrate (SPS). Before colonoscopy, information was collected regarding adherence with, and utility of the provided instructions. The colonoscopists, blinded to assignment, graded bowel preparation using the Aronchick, Ottawa, and Chicago preparation scales. The primary endpoint was adherence with instructions. Quality of preparation was a secondary endpoint. RESULTS: No difference in overall adherence or bowel cleanliness was observed between the study arms. Adherence was reported in 82.4% of App vs. 73.4% of controls (P=0.40). An adequate bowel preparation on the Aronchick scale was noted in 77.2 vs. 82.5%, respectively (P=0.68). Mean scores on the Ottawa and Chicago scales were also similar. Gender, age, time of colonoscopy, and BMI did not influence preparation or adherence. Compliance with the clear fluid diet component was noted in 94% of patients with BMI<30 vs. 77% with BMI≥30 (P<0.01). SPS was well tolerated by 81.9% of patients. The App was user-friendly and received higher overall rating in this respect than paper instructions (P<0.01). CONCLUSIONS: SPS is well tolerated and effective for bowel cleansing regardless of instruction method. Customized smartphone applications are effective, well-accepted and could replace standard paper instructions for bowel preparation.ClinicalTrials.gov: NCT02410720.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".