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Record W2791483463 · doi:10.1093/jcag/gwy009.044

A44 IS BLOOD UREA NITROGEN AN INDEPENDENT PREDICTOR OF POSITIVE ENDOSCOPIC FINDINGS IN PRESUMED UPPER GI BLEEDING?

2018· article· en· W2791483463 on OpenAlexaff
Naureen Narula, Deepti Chopra, M Rosenberg, Paul Moayyedi

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMelenaMedicineOdds ratioInternal medicineBlood urea nitrogenGastroenterologyLogistic regressionUrea breath testUpper gastrointestinal bleedingEndoscopyCreatinineSurgeryHelicobacter pylori

Abstract

fetched live from OpenAlex

Several prognostic scales have been developed for use in upper gastrointestinal bleeding (UGIB), including the Glasgow-Blatchford score, which uses blood urea nitrogen (BUN) as one of eight prognostic variables. However, the test characteristics of BUN in the identification of UGIB or high-risk endoscopic lesions have not been clearly determined. This study aimed to evaluate if BUN independently predicts the presence of positive endoscopic findings in cases of presumed UGIB and determine a threshold urea level above which it is more likely to identify a source of UGIB on endoscopy. A crude odds ratio was calculated for odds of bleeding being identified on upper endoscopy based on thresholds of urea>=5, 7.5, 10, 12.5 and 15, compared to values lower than these. Adjusted odds ratios were then calculated using logistic regression to account for the factors that were determined a priori. Covariates included in the model were age, sex, hemoglobin, presence of melena, presence of hematemesis, admission in ICU, and use of ASA, warfarin, clopidogrel, or NSAIDS. The odds of identifying UGIB at endoscopy for patients with a urea >=10 was 3.73 (95% CI: 1.90–7.31) times higher than for patients with urea < 10. Variables that were significantly associated with identifying source of bleeding at upper GI endoscopy included male gender and symptoms of melena or hematemesis, after adjusting for the impact of other covariates. BUN >20 is predictive of UGIB in the following settings: normal renal function (spec 98%, PPV 0.86), melena (spec 81%, PPV 0.8), NSAID+anticoagulation/ASA use (spec 85%, PPV 0.8), and cirrhotic patients (spec 100%, PPV 1.0). BUN >20 is also predictive of positive EGD findings (spec 87%, PPV 0.8) and high risk lesions (spec 81%, NPP 0.83). A BUN level >20 had 82% specificity for high risk endoscopic lesion requiring intervention, but lower BUN levels were not able to predict EGD intervention. BUN >15 is predictive of UGIB in hematemesis (spec 95%, PPV 0.95) or NSAID users (spec 100%, PPV 1.0). BUN levels >20 had a specificity of 87% for UGIB but poor sensitivity (23%), in contrast to the Glasgow-Blatchford score which is highly sensitive (>90%) but poorly specific (<20%). Overall, the results of this study provide new clinically relevant information regarding the operating characteristics of BUN for UGIB. In males, patients with normal renal function, cirrhosis, NSAID/ASA/AC users, or symptoms of melena and hematemesis, a high BUN level is predictive of positive endoscopic findings in presumed UGIB. A BUN level >20 predicts the need for endoscopic intervention but levels below 20 do not correlate well with the need for endoscopic intervention. BUN level alone is more specific for UGIB when compared to the Glasgow-Blatchford score, which has a higher sensitivity. 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)0.002
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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