Utility of the CANUKA Scoring System in the Risk Assessment of Upper GI Bleeding
Bibliographic record
Abstract
BACKGROUND: The Canada-United Kingdom-Adelaide (CANUKA) score was developed to stratify patients who experience upper gastrointestinal bleeding (UGIB) to predict who could be discharged from the emergency department. Our aim was to determine if the CANUKA score could be utilized for UGIB in-patients undergoing endoscopy in predicting adverse outcomes. We additionally sought to establish a CANUKA score cut point to predict adverse outcomes and in-hospital mortality and compare this to established scoring systems. METHODS: Between January 1, 2018 to June 30, 2019 all patients who underwent upper endoscopy after admission for UGIB were identified. We assigned a CANUKA score and compared the area under the receiver operating curve to established scoring systems. RESULTS: Our data set included 641 patients, with a mean age of 59.5±14.5 years. A CANUKA score ≥10 was associated with an adverse outcome [unadjusted odds ratio, 3.08 (1.79, 5.27)]. No patients experienced an adverse outcome with a CANUKA score <4. No patients died with a CANUKA score <6. Those with a CANUKA score of <10 had an in-hospital mortality of 2.1% compared with 6.8% for those with a score ≥10 ( P =0.008). AIMS65 had the best area under the receiver operating characteristic curve (0.809) for predicting mortality. CONCLUSIONS: The CANUKA score may serve utility as a predictor of adverse outcomes and mortality in patients admitted with UGIB undergoing endoscopy. Future studies, ideally prospective and multicenter, will be needed to validate its clinical utility.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".