Comparison of Canada-United Kingdom-Australia (CANUKA) scores of patients with gastrointestinal bleeding presenting to the emergency department with other gastrointestinal bleeding scores
Bibliographic record
Abstract
Background/aim: Acute gastrointestinal (GI) tract bleeding is a common and potentially life-threatening condition among patients presenting to emergency departments. In this study, we calculated AIMS65, Rockall, Glasgow-Blatchford Score (GBS), and Canada-United Kingdom-Australia (CANUKA) scores in patients with GI bleeding admitted to the emergency department and compared the sensitivity of these scoring systems in predicting the rates of admission to intensive care units and mortality. It is aimed to contribute to clinical practice and help determine an effective risk assessment tool in the management of patients with GI bleeding. Materials and methods: The study was conducted with patients who were diagnosed with GI bleeding. The study was conducted retrospectively between 1 January 2020 and 31 December 2023. The data of the patients were obtained from the hospital automation system. Patients with missing data were excluded from the study. AIMS65, Rockall, GBS, and CANUKA scores of the patients were calculated and recorded separately. Results: A total of 916 patients were included in our study. The median age was 70 years, and 62.3% of the patients were male. A total of 22.2% of the patients were hospitalized in the intensive care unit (ICU), and the in-hospital mortality rate was 0.9% (n = 8). According to the results of receiver operator characteristic (ROC) analysis of continuous measurements in terms of ICU hospitalization, the ability of the 4 scores to predict ICU hospitalization was statistically significant (p < 0.001). The CANUKA Score had the highest and best discriminative ability to predict ICU admission (area under the ROC curve [AUC] = 0.734). According to the results of ROC analysis of continuous measures in terms of mortality, the ability of AIMS65, CANUKA, and Rockall scores to predict mortality was statistically significant (p-values <0.001, <0.001, and 0.001, respectively). Conclusion: The CANUKA Score had the best discriminative ability in predicting intensive care unit admission and the best discriminative ability in predicting mortality after the AIMS65 Score.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".