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Record W3165503655 · doi:10.1186/s12876-021-01817-2

A multicenter, prospective, inpatient feasibility study to evaluate the use of an intra-colonoscopy cleansing device to optimize colon preparation in hospitalized patients: the REDUCE study

2021· article· en· W3165503655 on OpenAlexfundno aff
Helmut Neumann, Melissa Latorre, Tim Zimmerman, Gabriel Lang, Jason Samarasena, Seth A. Gross, Bhaumik Brahmbhatt, Haleh Pazwash, Vladimir Kushnir

Bibliographic record

VenueBMC Gastroenterology · 2021
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
FundersUniversitätsmedizin der Johannes Gutenberg-Universität MainzYork UniversityNYU Langone Medical Center
KeywordsMedicineColonoscopyHepatologyMulticenter studyProspective cohort studyInternal medicineCatharticColorectal surgeryEmergency medicineIntensive care medicineGeneral surgeryAbdominal surgeryColorectal cancerRandomized controlled trial

Abstract

fetched live from OpenAlex

BACKGROUND: High quality bowel preparation prior to colonoscopy can be difficult to achieve in the inpatient setting. Hospitalized patients are at risk for extended hospital stays and low diagnostic yield due to inadequate bowel preparation. The Pure-Vu System is a novel device intended to fit over existing colonoscopes to improve intra-colonoscopy bowel preparation. The objective of the REDUCE study was to conduct the first inpatient study to evaluate optimization of bowel preparation quality following overnight preparation when using the Pure-Vu System during colonoscopy. METHODS: This multicenter, prospective feasibility study enrolled hospitalized subjects undergoing colonoscopy. Subjects recorded the clarity of their last bowel movement using a 5-point scale prior to colonoscopy. After one night of preparation, all enrolled subjects underwent colonoscopy utilizing the Pure-Vu System. The primary endpoint was improvement of colon cleanliness from baseline to post-cleansing with the Pure-Vu System as assessed by the improvement in Boston Bowel Preparation Scale (BBPS). An exploratory analysis was conducted to assess whether the clarity of the last bowel movement could predict inadequate bowel preparation. RESULTS: Ninety-four subjects were included. BBPS analyses showed significant improvements in bowel preparation quality across all evaluable colon segments after cleansing with Pure-Vu, including left colon (1.74 vs 2.89; p < 0.0001), transverse colon (1.74 vs 2.91; p < 0.0001), and the right colon (1.41 vs 2.88; p < 0.0001). Prior to Pure-Vu, adequate cleansing (BBPS scores of ≥ 2) were reported in 60%, 62%, and 47% for the left colon, transverse colon, and right colon segments, respectively. After intra-colonoscopy cleansing with the Pure-Vu System, adequate colon preparation was reported in 100%, 99%, and 97% of the left colon, transverse colon, and right colon segments, respectively. Subjects with lower bowel movement clarity scores were more likely to have inadequate bowel preparation prior to cleansing with Pure-Vu. CONCLUSIONS: In this feasibility study, the Pure-Vu System appears to be effective in significantly improving bowel preparation quality in hospitalized subjects undergoing colonoscopy. Clarity of last bowel movement may be useful indicator in predicting poor bowel preparation. Larger studies powered to evaluate clinical outcomes, hospital costs, and blinded BBPS assessments are required to evaluate the significance of these findings. Trial registration Evaluation of the Bowel Cleansing in Hospitalized Patients Using Pure-Vu System (NCT03503162).

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.362
Teacher spread0.298 · 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 designNon-randomized 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

Citations10
Published2021
Admission routes1
Has abstractyes

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