The Effectiveness of Personalized Bowel Preparation Using a Smartphone Camera Application: A Randomized Pilot Study
Bibliographic record
Abstract
Background. We aimed to investigate the effectiveness of a smartphone application that analyzes and judges the optimal dosage of polyethylene glycol (PEG) for bowel preparation. Methods. Patients were assigned to use the smartphone camera application (app group) or written instructions (non-app group). The smartphone camera application was programmed to analyze the bowel preparation quality and automatically determine the dosage of PEG from an analysis of stool images. In contrast, the non-app group consumed PEG solution according to the manual. Results. The primary outcome was the quality of the bowel preparation based on blinded ratings using the Ottawa bowel preparation scale (OBPS). There was no statistically significant difference in the mean OBPS scores between the two groups ( P=0.950 ). However, the app group consumed a lower dose of PEG than the non-app group (mean dosage (mL): 3713.2 ± 405.8 versus 3979.2 ± 102.06, P=0.001 ). The app group (5-point Likert scale; mean score 4.37 ± 0.895) had high acceptance of the application. Conclusions. Although the app group consumed a lower PEG dose, the bowel preparation quality was similar in the two groups. Moreover, use of the smartphone camera application enhanced compliance with the bowel preparation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 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".