Validation of the Rockall scoring system for outcomes from non-variceal upper gastrointestinal bleeding in a Canadian setting
Bibliographic record
Abstract
AIM: To validate the Rockall scoring system for predicting outcomes of rebleeding, and the need for a surgical procedure and death. METHODS: We used data extracted from the Registry of Upper Gastrointestinal Bleeding and Endoscopy including information of 1869 patients with non-variceal upper gastrointestinal bleeding treated in Canadian hospitals. Risk scores were calculated and used to classify patients based on outcomes. For each outcome, we used chi2 goodness-of-fit tests to assess the degree of calibration, and built receiver operating characteristic curves and calculated the area under the curve (AUC) to evaluate the discriminative ability of the scoring system. RESULTS: For rebleeding, the chi2 goodness-of-fit test indicated an acceptable fit for the model [chi2 (8) = 12.83, P = 0.12]. For surgical procedures [chi2 (8) = 5.3, P = 0.73] and death [chi2 (8) = 3.78, P = 0.88], the tests showed solid correspondence between observed proportions and predicted probabilities. The AUC was 0.59 (95% CI: 0.55-0.62) for the outcome of rebleeding and 0.60 (95% CI: 0.54-0.67) for surgical procedures, representing a poor discriminative ability of the scoring system. For the outcome of death, the AUC was 0.73 (95% CI: 0.69-0.78), indicating an acceptable discriminative ability. CONCLUSION: The Rockall scoring system provides an acceptable tool to predict death, but performs poorly for endpoints of rebleeding and surgical procedures.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".