Improving outcomes from acute upper gastrointestinal bleeding: Table 1
Bibliographic record
Abstract
Upper gastrointestinal bleeding (UGIB) is predominantly non-variceal in origin and remains one of the most common challenges faced by gastroenterologists and endoscopists in daily clinical practice. Despite major advances in the approach to the management of non-variceal upper gastrointestinal bleeding (NVUGIB) over the past decade including prevention of peptic ulcer bleeding, optimal use of endoscopic therapy1 and high-dose proton pump inhibition,2 it still carries considerable morbidity, mortality and health economic burden. Although many modernised healthcare systems report reductions in case death over time,3–5 most likely attributable to the aforementioned advances in addition to general supportive care, mortality remains appreciably high. Of particular note, rebleeding rates—one of the most important predictive factors of mortality and arguably the best reflection of interventions directly targeted at bleeding—have not significantly improved from longitudinal data in the past 15 years.6 7 In addition to high-quality trials that have informed best evidence-based practice guidelines in recent years,8 we have been provided with a plethora of detailed ‘real-life’ outcome data from multicentre observational studies of UGIB originating from Canada (RUGBE,9 REASON10 AND REASON-211), Italy (PNED-112 and PNED-213) and the UK,14 enabling an assessment of how well such guidelines are adhered to and serving to highlight deficiencies in existing aspects of care which could be improved upon. These studies are further described in table 1. View this table: Table 1 Description of study designs and inclusion criteria Using data from published papers of the aforementioned studies, we highlight a number of areas in the management of NVUGIB where efforts could be targeted to improve existing shortcomings in care. In addition we provide suggestions for parameters upon which improvements in care may be monitored in longitudinal studies as well as highlighting key areas for …
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".