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Record W2790140452 · doi:10.1093/jcag/gwy009.208

A208 OPTIMIZING THE UTILITY OF CT ENTEROGRAPHY FOR THE EVALUATION OF OBSCURE GASTROINTESTINAL BLEEDING: A NOVEL HIGHLY SENSITIVE CLINICAL PREDICTION TOOL

2018· article· en· W2790140452 on OpenAlexaffabout
Kristel Leung, Usman Khan, Jeffrey D. McCurdy, Paul D. James

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsUniversity of TorontoUniversity Health NetworkOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineCapsule endoscopyObscure gastrointestinal bleedingLogistic regressionInternal medicineUnivariate analysisColonoscopyBleedRadiologyMultivariate analysisColorectal cancerSurgeryCancer

Abstract

fetched live from OpenAlex

Computed tomography (CTE) and capsule endoscopy (CE) are common modalities used to investigate obscure GI bleeds (OGIB). Although recent guidelines recommend CE before CTE as the first modality to evaluate OGIB, CTE offers multiple advantages including lower cost, greater availability, and the ability to detect strictures, masses, and extraluminal pathology. It is not yet clear which OGIB patients would most benefit from CTE before CE. Our study sought to determine patient factors associated with positive CTE in OGIB patients to develop a novel clinical decision tool to predict which patients are more likely versus less likely to have a diagnostic CTE. This was a retrospective study using patients who underwent CTE for OGIB defined as a suspected gastrointestinal (GI) bleed with no cause identified on gastroscope or colonoscopy at The Ottawa Hospital between 2005- 2015. Factors (symptoms, history, investigations, interventions, outcomes) selected a priori from literature review were collected by chart review. Logistic regression with univariate and multivariate analysis were performed to identify factors associated with a positive CTE study. Of 147 patients with OGIB, CTE was positive in 1 in 5 cases (n=31, 21%). 22 (71%) of the CTE positive cases had at least one of intestinal wall thickening, angiodysplasias, suspected bowel mass, or concerning stricture. The presence of overt GI bleeding (OR 12.4, 95% CI 1.6–95.0), abdominal or constitutional symptoms (OR 2.9, 95% CI 1.2–7.0), or a personal history of GI cancer (OR 17.5, 95% CI 1.9–163.5) were factors that predicted a positive CTE study with univariate analysis. This was also seen in multivariate analysis (OR 13.3, 95% CI 1.5–121.9; OR 2.5, 95% CI 1.0–6.6; OR 30.3, 95% CI 1.4–634.2 respectively). Absence of these 3 factors was associated with zero likelihood of having a positive CTE, while presence of any of these factors was associated with a 1 in 4 likelihood of having a positive CTE. A rule based on the absence of these factors for predicting a positive CTE would have a sensitivity, specificity, negative predictive value, and positive predictive value of 100% (95% CI 80–100%), 16% (95% CI 10–24%), 100%, and 24% (95% CI 23–26%) respectively. CTE can be diagnostic in 1 in 5 cases of OGIB. Diagnostic yield may be greater for patients with overt GI bleeding, abdominal and/or constitutional symptoms, or a personal history of GI malignancy. Absence of these 3 factors was associated with zero likelihood of a positive CTE. Together, these factors can function as a highly sensitive tool to predict which OGIB patients are unlikely to have a diagnostic CTE. This can potentially reduce non-diagnostic CTE and avoid the need for CE. Future research to validate this tool in other populations with OGIB are needed. None

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.322
Teacher spread0.264 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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