Limits of pre-endoscopic scoring systems in geriatric patients with upper gastrointestinal bleeding
Bibliographic record
Abstract
Upper gastrointestinal bleeding (UGIB) is a common cause of hospital admission worldwide and several risk scores have been developed to predict clinically relevant outcomes. Despite the geriatric population being a high-risk group, age is often overlooked in the assessment of many risk scores. In this study we aimed to compare the predictive accuracy of six pre-endoscopic risk scoring systems in a geriatric population hospitalised with UGIB. We conducted a multi-center cross-sectional study and recruited 136 patients, 67 of these were 65-81.9 years old ("< 82 years"), 69 were 82-100 years old ("≥ 82 years"). We performed six pre-endoscopic risk scores very commonly used in clinical practice (i.e. Glasgow-Blatchford Bleeding and its modified version, T-score, MAP(ASH), Canada-United Kingdom-Adelaide, AIMS65) in both age cohorts and compared their accuracy in relevant outcomes predictions: 30-days mortality since hospitalization, a composite outcome (need of red blood transfusions, endoscopic treatment, rebleeding) and length of hospital stay. T-score showed a significantly worse performance in mortality prediction in the "≥ 82 years" group (AUROC 0.53, 95% CI 0.27-0.75) compared to "< 82 years" group (AUROC 0.88, 95% CI 0.77-0.99). In the composite outcome prediction, except for T-score, younger participants had higher sensitivities than those in the "≥ 82 years" group. All risk scores showed low performances in the prediction of length of stay (AUROCs ≤ 0.70), and, except for CANUKA score, there was a significant difference in terms of accuracy among age cohorts. Most used UGIB risk scores have a low accuracy in the prediction of clinically relevant outcomes in the geriatric population; hence novel scores should account for age or advanced age in their assessment.
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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 | 0.007 | 0.021 |
| 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".