Risk Stratification in Cancer Patients with Acute Upper Gastrointestinal Bleeding: Comparison of Glasgow-Blatchford, Rockall and AIMS65, and Development of a New Scoring System
Bibliographic record
Abstract
BACKGROUND/AIMS: Few studies have measured the accuracy of prognostic scores for upper gastrointestinal bleeding (UGIB) among cancer patients. Thereby, we compared the prognostic scores for predicting major outcomes in cancer patients with UGIB. Secondarily, we developed a new model to detect patients who might require hemostatic care. METHODS: A prospective research was performed in a tertiary hospital by enrolling cancer patients admitted with UGIB. Clinical and endoscopic findings were obtained through a prospective database. Multiple logistic regression analysis was performed to gauge the power of each score. RESULTS: From April 2015 to May 2016, 243 patients met the inclusion criteria. The AIMS65 (area under the curve [AUC] 0.85) best predicted intensive care unit admission, while the Glasgow-Blatchford score best predicted blood transfusion (AUC 0.82) and the low-risk group (AUC 0.92). All scores failed to predict hemostatic therapy and rebleeding. The new score was superior (AUC 0.74) in predicting hemostatic therapy. The AIMS65 (AUC 0.84) best predicted in-hospital mortality. CONCLUSION: The scoring systems for prognostication were validated in the group of cancer patients with UGIB. A new score was developed to predict hemostatic therapy. Following this result, future prospective research should be performed to validate the new score.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".