Does Polyethylene Glycol (PEG) Plus Ascorbic Acid Induce More Mucosal Injuries than Split-Dose 4-L PEG during Bowel Preparation?
Bibliographic record
Abstract
BACKGROUND/AIMS: The aims of this study were to compare the bowel-cleansing efficacy, patient affinity for the preparation solution, and mucosal injury between a split dose of polyethylene glycol (SD-PEG) and low-volume PEG plus ascorbic acid (LV-PEG+Asc) in outpatient scheduled colonoscopies. METHODS: Of the 319 patients, 160 were enrolled for SDPEG, and 159 for LV-PEG+Asc. The bowel-cleansing efficacy was rated according to the Ottawa bowel preparation scale. Patient affinity for the preparation solution was assessed using a questionnaire. All mucosal injuries observed during colonoscopy were biopsied and histopathologically reviewed. RESULTS: There was no significant difference in bowel cleansing between the groups. The LV-PEG+Asc group reported better patient acceptance and preference. There were no significant differences in the incidence or characteristics of the mucosal injuries between the two groups. CONCLUSIONS: Compared with SD-PEG, LV-PEG+Asc exhibited equivalent bowel-cleansing efficacy and resulted in improved patient acceptance and preference. There was no significant difference in mucosal injury between SD-PEG and LV-PEG+Asc. Thus, the LV-PEG+Asc preparation could be used more effectively and easily for routine colonoscopies without risking significant mucosal injury.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".