Reassessment of Rebleeding Risk of Forrest IB (Oozing) Peptic Ulcer Bleeding in a Large International Randomized Trial
Bibliographic record
Abstract
OBJECTIVES: Our aims were to assess risks of early rebleeding after successful endoscopic hemostasis for Forrest oozing (FIB) peptic ulcer bleeding (PUBs) compared with other stigmata of recent hemorrhage (SRH). METHODS: These were post hoc multivariable analyses of a large, international, double-blind study (NCT00251979) of patients randomized to high-dose intravenous (IV) esomeprazole (PPI) or placebo for 72 h. Rebleeding rates of patients with PUB SRH treated with either PPI or placebo after successful endoscopic hemostasis were also compared. RESULTS: For patients treated with placebo for 72 h after successful endoscopic hemostasis, rebleed rates by SRH were spurting arterial bleeding (FIA) 22.5%, adherent clot (FIIB) 17.6%, non-bleeding visible vessel (FIIA) 11.3%, and oozing bleeding (FIB) 4.9%. Compared with FIB patients, FIA, FIIB, and FIIA had significantly greater risks of rebleeding with odds ratios (95% CI's) from 2.61 (1.05, 6.52) for FIIA to 6.66 (2.19, 20.26) for FIA. After hemostasis, PUB rebleeding rates for FIB patients at 72 h were similar with esomeprazole (5.4%) and placebo (4.9%), whereas rebleed rates for all other major SRH (FIA, FIIA, FIIB) were lower for PPI than placebo, but the treatment by SRH interaction test was not statistically significant. CONCLUSIONS: After successful endoscopic hemostasis, FIB patients had very low PUB rebleeding rates irrespective of PPI or placebo treatment. This implies that after successful endoscopic hemostasis the prognostic classification of FIB ulcers as a high-risk SRH and the recommendation to treat these with high-dose IV PPI's should be re-evaluated.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".