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Record W171457665

Effect of intravenous Buscopan on colonic distention during computed tomography colonography.

2008· article· en· W171457665 on OpenAlexaff
Carola Behrens, Giles W. Stevenson, Richard Eddy, John Mathieson

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineDiverticular diseaseCecumAscending colonDiverticulosisSigmoid colonRectumColonoscopyComputed tomographyRadiologyInternal medicineColorectal cancer
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: This study was designed to assess whether spasmolytic drugs are helpful in computed tomography colonography (CTC), as there is conflict in the literature. METHOD: We assessed retrospectively in a blinded fashion colonic distention in 149 individuals, one-half of whom had intravenous (IV) Buscopan during CTC. Colonic segments (n = 1788) were analyzed by 2 observers, and allocated to one of 4 grades of distention. We also recorded the presence and severity of diverticular disease. RESULTS: Buscopan increased the likelihood of optimal distention by an OR of 5 when considering individual colonic segments from ascending colon to sigmoid, with little effect on rectum or cecum. Considering the colon as a whole, the OR of optimal distention occurring throughout the entire colon was 7.9 times greater with Buscopan than without. In the sigmoid colon, Buscopan had a significantly greater impact on obtaining optimal distention in those with diverticulosis than in those without. CONCLUSION: Buscopan increases the probability of obtaining optimal distention during CTC, especially in the sigmoid colon in diverticular disease. Buscopan is likely to improve polyp conspicuity and patient comfort, and to reduce both the examination time and the interpretation time. We recommend routine use of Buscopan during CTC.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations12
Published2008
Admission routes1
Has abstractyes

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