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S0531 What Is the Ideal Dietary Regimen for Patients Scheduled for Colonoscopy: Low Residue Diet or Clear Liquid Diet? A Meta-Analysis and Systematic Review of Randomized Controlled Trials

2020· article· en· W3093505488 on OpenAlexaboutno aff
Nabil El Hage Chehade, Alexander Abadir, Zain Moosvi, Sagar Shah, Jason Samarasena

Bibliographic record

VenueThe American Journal of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsColonoscopyMedicineTolerabilityMeta-analysisRandomized controlled trialOdds ratioInternal medicineBowel preparationColorectal cancerRegimenAdverse effectIncidence (geometry)GastroenterologyCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: Colonoscopy screening and early detection of pre-cancerous polyps can significantly reduce both the incidence and mortality of colorectal cancer. The quality of bowel preparation before a colonoscopy is an important predictor of lesion identification. However, patients often cite bowel preparation and associated dietary restrictions as the greatest deterrents to having a colonoscopy. Traditionally, a clear liquid diet (CLD) is required the day before colonoscopy. Recent studies have investigated the use of a low-residue diet (LRD), with varying results. We evaluated various outcomes in patients undergoing colonoscopy who either consumed a CLD versus LRD on the day before colonoscopy. METHODS: A literature search was performed on Scopus, PubMed/MEDLINE, and Cochrane databases (April 2020). Studies included in our meta-analysis involved adult patients undergoing colonoscopy who either consumed a LRD or CLD on the day prior to colonoscopy. Analysis was conducted with the Mantel-Haenszel model using the odds ratio (OR) to assess adequate bowel preparations, tolerability, willingness to repeat diet and preparation, compliance with diet, adenoma and polyp detection rate, and overall adverse effects. RESULTS: 17 studies (4340 patients) were included the analysis. Findings suggest that consuming a LRD demonstrated no differences in terms of adequate bowel preparations (OR 1.31; 95% CI, 0.92–1.86; P = 0.14), compared with a CLD. Subgroup analysis was performed based on the scale used to assess the quality of the bowel preparation (Boston Bowel Preparation Scale, Ottawa Bowel Preparation Scale, and Aronchick Scale) and demonstrated non-inferiority of each. There was higher tolerability (OR 2.21; 95% CI, 1.64–2.98; P < 0.0001) and willingness to repeat preparation (OR 1.90; 95% CI, 1.36–2.67; P = 0.0002) with no differences in compliance with dietary regimen (OR = 1.17; 95% CI, 0.60–2.27; P = 0.64), adenoma detection rate (OR = 1.01; 95% CI, 0.86–1.19; P = 0.92), polyp detection rate (OR = 0.87; 95% CI, 0.87–1.04; P = 0.13), or overall adverse effects (OR 0.90; 95% CI, 0.73–1.13; P = 0 0.37). CONCLUSION: A low residue diet compared to a clear liquid diet prior to colonoscopy resulted in improved patient tolerability and willingness to repeat preparation with no differences in preparation quality. As a less restrictive dietary regimen, the low-residue diet may help improve patient participation in colorectal cancer screening programs.Figure 1.: Forest plot comparing the frequency of adequate bowel preparations while on a low-residue diet compared with a clear liquid diet consumed on the day prior to colonoscopy, performed with a sub-group analysis according to the scale used to determine adequacy of the preparation. BBPS, Boston Bowel Preparation Scale; CI, confidence interval; M-H, Mantel-Haenszel; OBPS, Ottawa Bowel Preparation Scale.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0190.033
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.044
GPT teacher head0.332
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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