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Record W2333996394 · doi:10.1155/2016/3181459

Split-Dose Polyethylene Glycol Is Superior to Single Dose for Colonoscopy Preparation: Results of a Randomized Controlled Trial

2016· article· en· W2333996394 on OpenAlexaffabout
Rachid Mohamed, Robert J. Hilsden, Catherine Dubé, Alaa Rostom

Bibliographic record

VenueCanadian Journal of Gastroenterology and Hepatology · 2016
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of OttawaAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsMedicineColonoscopyPEG ratioTolerabilityRandomized controlled trialCatharticProspective cohort studyRandomizationClinical trialInternal medicineBowel preparationPolyethylene glycolSurgeryGastroenterologyAdverse effectColorectal cancerCancer

Abstract

fetched live from OpenAlex

Background. The efficacy of colonoscopy in detecting abnormalities within the colon is highly dependent on the adequacy of the bowel preparation. The objective of this study was to compare the efficacy, safety, and tolerability of PEG lavage and split-dose PEG lavage with specific emphasis on the cleanliness of the right colon. Methods. The study was a prospective, randomized, two-arm, controlled trial of 237 patients. Patients between the age of 50 and 75 years were referred to an outpatient university screening clinic for colonoscopy. Patients were allocated to receive either a single 4 L PEG lavage or a split-dose PEG lavage. Results. Overall, the bowel preparation was superior in the split-dose group compared with the single-dose group (mean Ottawa score 3.50 ± 2.89 versus 5.96 ± 3.53; P < 0.05) and resulted in less overall fluid in the colon. This effect was observed across all segments of the colon assessed. Conclusions. The current study supports use of a split-dose PEG lavage over a single large volume lavage for superior bowel cleanliness, which may improve polyp detection. This trial is registered with ClinicalTrials.gov identifier NCT01610856.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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 designRandomized trial
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".

Quick stats

Citations35
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Gastroenterology and HepatologySame topicColorectal Cancer Screening and DetectionFrench-language works237,207