Do adjuvants add to the efficacy and tolerance of bowel preparations? A meta-analysis of randomized trials
Bibliographic record
Abstract
BACKGROUND AND STUDY AIMS : Recommendations on adjuvant use with bowel preparations remain disparate. We performed a meta-analysis determining the clinical impact of adding an adjuvant to polyethylene glycol (PEG), sodium phosphate, picosulfate (PICO), or oral sulfate solutions (OSS)-based regimens. METHODS: Systematic searches were made of MEDLINE, EMBASE, Scopus, CENTRAL and ISI Web of knowledge for randomized trials from January 1980 to April 2016 that assessed preparations with or without adjuvants, given in split and non-split dosing, and PEG high- (> 3 L) or low-dose (≤ 2 L) regimens. Bowel cleansing efficacy was the primary outcome. Secondary outcomes included patient willingness to repeat the procedure, and polyp and adenoma detection rates. RESULTS: Of 3093 citations, 77 trials fulfilled the inclusion criteria. Overall, addition of an adjuvant compared with no adjuvant, irrespective of the type of preparation and mode of administration, yielded improvements in bowel cleanliness (odds ratio [OR] 1.23 [1.01 - 1.51]) without greater willingness to repeat (OR 1.40 [0.91 - 2.15]). Adjuvants combined with high-dose PEG significantly improved colon cleansing (OR 1.96 [1.32 - 2.94]). The odds for achieving adequate preparation with low-dose PEG with an adjuvant were not different to high-dose PEG alone (OR 0.95 [0.73 - 1.22]), but yielded improved tolerance (OR 3.22 [1.85 - 5.55]). However, split high-dose PEG yielded superior cleanliness to low-dose PEG with adjuvants (OR 2.53 [1.25 - 5.13]). No differences were noted for OSS and PICO comparisons, or for any products regarding polyp or adenoma detection rates. CONCLUSIONS: Critical heterogeneity precludes firm conclusion on the impact of adjuvants with existing bowel preparations. Additional research is required to better characterize the methods of administration and resulting roles of adjuvants in an era of split-dosing.
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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 | 0.041 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.064 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".