COMPARATIVE ANALYSIS OF COLON PREPARATION USING POLY ETHYLENE GLYCOL (PEG) WITH LACTULOSE FOR COLONOSCOPY
Bibliographic record
Abstract
Objective: To compare the adequacy of preparation for colonoscopy using polyethylene glycol with lactulose. Study Design: Quasi experimental study. Place and Duration of Study: Department of Gastroenterology, Combined Military Hospital Lahore, from Jul 2019 to Dec 2019. Methodology: The enrolled patients were randomized into two equal groups. Group A received polyethylene glycol (PEG) formulation containing 13.125g x 24 sachets with 4 liters of water over 24 hours, group B received lactulose 600ml (66.7g/100ml) with 4 liters of water over 24 hours. Quality of bowel preparation was assessed using Ottawa scoring system (OBPS). A designed questionnaire was used for recording patients’ tolerability toward the preparation method. Questionnaire was filled pre and post-procedure for socio-demographic data and scores. Patients were also assessed for tolerability of procedure and palatability of the solutions by questionnaire. Results: The median age of patients in group A was 49 (26 IQR) years and in group B was 48 (18.5 IQR) years. Out of 100 patients 60% were males and 40% were females. The most common indication of colonoscopy was anemia (32%), followed by diarrhea (26%), bleeding per-rectum (24%) and constipation (18%). Polyethylene glycol showed better bowel cleansing score compared with lactulose (p<0.001). Tolerability and palatability for polyethylene glycol were statistically superior to the same volume of lactulose. Conclusion: Polyethylene glycol is superior to lactulose in terms of bowel preparation, tolerability and palatability.
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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) | 0.002 | 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".