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Record W2031674842 · doi:10.5009/gnl13228

Importance of the Time Interval between Bowel Preparation and Colonoscopy in Determining the Quality of Bowel Preparation for Full-Dose Polyethylene Glycol Preparation

2014· article· en· W2031674842 on OpenAlexaboutno aff
Tae Kyoung Kim, Hyung Wook Kim, Su Jin Kim, Jong Kun Ha, Hyung Ha Jang, Young Mi Hong, Su Bum Park, Cheol Woong Choi, Dae Hwan Kang

Bibliographic record

VenueGut and Liver · 2014
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
FundersPusan National University
KeywordsMedicineColonoscopyBowel preparationConfidence intervalOdds ratioPEG ratioSurgeryInternal medicineColorectal cancer

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: The quality of bowel preparation (QBP) is the important factor in performing a successful colonoscopy. Several factors influencing QBP have been reported; however, some factors, such as the optimal preparation-to-colonoscopy time interval, remain controversial. This study aimed to determine the factors influencing QBP and the optimal time interval for full-dose polyethylene glycol (PEG) preparation. METHODS: A total of 165 patients who underwent colonoscopy from June 2012 to August 2012 were prospectively evaluated. The QBP was assessed using the Ottawa Bowel Preparation Scale (Ottawa) score according to several factors influencing the QBP were analyzed. RESULTS: Colonoscopies with a time interval of 5 to 6 hours had the best Ottawa score in all parts of the colon. Patients with time intervals of 6 hours or less had the better QBP than those with time intervals of more than 6 hours (p=0.046). In the multivariate analysis, the time interval (odds ratio, 1.897; 95% confidence interval, 1.006 to 3.577; p=0.048) was the only significant contributor to a satisfactory bowel preparation. CONCLUSIONS: The optimal time was 5 to 6 hours for the full-dose PEG method, and the time interval was the only significant contributor to a satisfactory bowel preparation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.332
Teacher spread0.307 · 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 designObservational
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

Citations21
Published2014
Admission routes1
Has abstractyes

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