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Record W2747121196 · doi:10.3748/wjg.v23.i32.5994

Systematic review and meta-analysis of colon cleansing preparations in patients with inflammatory bowel disease

2017· review· en· W2747121196 on OpenAlexaff
Sophie Restellini, Omar Kherad, Talat Bessissow, Charles Ménard, Myriam Martel, Maryam Taheri Tanjani, Péter L. Lakatos, Alan Barkun

Bibliographic record

VenueWorld Journal of Gastroenterology · 2017
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsQueen's UniversityMcGill University Health CentreUniversité de Sherbrooke
FundersBoston Scientific Corporation
KeywordsMedicineInternal medicineMeta-analysisPopulationInflammatory bowel diseaseRandomized controlled trialPEG ratioTolerabilityRegimenGastroenterologyAdverse effectSurgeryDisease

Abstract

fetched live from OpenAlex

AIM: To performed a systematic review and meta-analysis to determine any possible differences in terms of effectiveness, safety and tolerability between existing colon-cleansing products in patients with inflammatory bowel disease. METHODS: Systematic searches were performed (January 1980-September 2016) using MEDLINE, EMBASE, Scopus, CENTRAL and ISI Web of knowledge for randomized trials assessing preparations with or without adjuvants, given in split and non-split dosing, and in high (> 3 L) or low-volume (2 L or less) regimens. Bowel cleansing quality was the primary outcome. Secondary outcomes included patient willingness-to-repeat the procedure and side effects/complications. RESULTS: high-volume; OR = 5.11 (1.31-20.0). CONCLUSION: In inflammatory bowel disease population, PEG low-volume regimen seems not inferior to PEG high-volume to clean the colon, and yields improved willingness-to-repeat. Further additional research is urgently required to compare contemporary products in this population.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.739
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.336
Teacher spread0.291 · 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 teacher head, not a consensus.

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

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".

Quick stats

Citations23
Published2017
Admission routes1
Has abstractyes

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