Adding stewed apricot juice to sena improves the right-side and overall colon cleansing quality for colonoscopy preparation
Bibliographic record
Abstract
Background:To achieve optimal colonoscopic examination, the bowel must be sufficiently cleansed. However, none of the currently available colonoscopy preparation regimens is safe, efficient, and comfortable. The aim of this study was to determine whether adding stewed apricot juice to senna increased patient comfort and improved bowel cleansing during colonoscopy preparation.Methods: Outpatients of both genders who were over 18 years old and were referred for elective colonoscopy were randomly allocated to drink stewed apricot juice with senna or senna by itself. The quality of the colon cleansing was evaluated using the Ottawa scale. Patient tolerance and adverse events were evaluated through the completion of a questionnaire. Results: The study included a total of 128 patients in the randomization procedure. A significantly greater cleansing effect was observed using stewed apricot juice plus senna in the right and transverse colon (p = 0.038, p = 0.037 respectively). It was also determined that in the stewed apricot juice plus senna group, overall cleansing was superior (p < 0.001), total colonoscopy (17.6 min vs. 22.8 min, p = 0.048) and cecal intubation (7.4 min vs. 11.2 min, p = 0.042) times were shorter, and the colonoscopy procedure was easier (79.4% vs. 49.2%, p < 0.001). No differences were observed between the groups with respect to patient acceptance, compliance, and events. In the stewed apricot juice plus senna group, 91.2% of patients stated their willingness to receive the same regimen in the future compared to 80% of the patients in the senna alone group (p = 0.037).Conclusion:The addition of natural, stewed apricot juice to senna significantly improves cleansing outcomes without additional adverse effects.Clinical trial registration number isNCT02665624, and the validity date is 24.01.2016.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".