Is Blood Urea Concentration an Independent Predictor of Positive Endoscopic Findings in Presumed Upper Gastrointestinal Bleeding?
Bibliographic record
Abstract
BACKGROUND: The test characteristics of blood urea concentration in the identification of upper gastrointestinal bleeding (UGIB) or high-risk endoscopic lesions have not been clearly determined. This study aimed to elucidate if urea independently correlates with the presence of positive endoscopic findings in cases of presumed UGIB and understand the diagnostic value of this parameter when assessing a patient with potential UGIB. METHODS: A retrospective cohort study was conducted at Hamilton Health Sciences hospitals examining patients who had upper endoscopy for presumed UGIB. Contingency tables were generated to determine the test characteristics of urea at different thresholds for prediction of UGIB. A crude OR was calculated for odds of bleeding being identified on endoscopy based on varying thresholds of urea, and adjusted ORs were calculated using logistic regression modelling. RESULTS: Variables significantly associated with detecting a source of GI bleeding at endoscopy included increase in urea (OR 1.06, 95% CI 1.01-1.09), male gender (OR 2.02, 95% CI 1.08-3.77), presence of melena (OR 2.37, 95% CI 1.06-5.33), and hematemesis (OR 3.88, 95% CI 1.70-8.83), when adjusted for other covariates. The odds of identifying UGIB at endoscopy in patients with urea ≥10 mmol/L was 3.73 (95% CI 1.90-7.31) times higher than for patients with urea <10 mmol/L. CONCLUSION: Urea level is an independent predictor of positive endoscopic findings in presumed UGIB, and urea ≥10 mmol/L may be a useful threshold to help guide clinicians towards clinically significant bleeding that could warrant early endoscopic evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".