History of malignancy and relevant symptoms may predict a positive computed tomography enterography in obscure gastrointestinal bleeds
Bibliographic record
Abstract
BACKGROUND AND AIM: This study aimed to assess the clinical utility of computed tomography enterography (CTE) and identify factors associated with a diagnostic CTE for patients with obscure gastrointestinal bleeding (OGIB). METHODS: A retrospective observational study was performed at a Canadian tertiary care center from 2005 to 2015. A total of 138 patients underwent a CTE for OGIB. Univariate and multivariate logistic regressions were performed to determine factors associated with a diagnostic CTE. A highly sensitive clinical rule was then developed to help identify OGIB patients for whom a CTE may be beneficial in their clinical work-up. RESULTS: A possible bleeding source was identified in 30 (22%) cases. The presence of abdominal or constitutional symptoms as well as history of colorectal cancer was significantly associated with a positive CTE in univariate and multivariate analyses (P < 0.05). A positive CTE could be predicted based on the presence of abdominal or constitutional symptoms and history of colorectal cancer with 90% sensitivity (95% CI 74-98%) in our population. CONCLUSION: CTE identified a possible source of OGIB in one in five cases. In patients with the presence of abdominal or constitutional symptoms and a personal history of colorectal cancer, CTE may contribute to their diagnostic work-up.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".