Meta-analysis: predictors of rebleeding after endoscopic treatment for bleeding peptic ulcer
Bibliographic record
Abstract
BACKGROUND: Determining the risk of rebleeding after endoscopic therapy for peptic ulcer bleeding (PUB) may be useful for establishing additional haemostatic measures in very high-risk patients. AIM: To identify predictors of rebleeding after endoscopic therapy. METHODS: Bibliographic database searches were performed to identify studies assessing rebleeding after endoscopic therapy for PUB. All searches and data abstraction were performed in duplicate. A parameter was considered to be an independent predictor of rebleeding when it was detected as prognostic by multivariate analyses in ≥2 studies. Pooled odds ratios (pOR) were calculated for prognostic variables. RESULTS: Fourteen studies met the prespecified inclusion criteria. Pre-endoscopic predictors of rebleeding were: (i) Haemodynamic instability: significant in 9 of 13 studies evaluating the variable (pOR: 3.30, 95% CI: 2.57-4.24); (ii) Haemoglobin value: significant in 2 of 10 (pOR: 1.73, 95% CI: 1.14-2.62) and (iii) Transfusion: significant in two of six (pOR not calculable). Endoscopic predictors of rebleeding were: (i) Active bleeding: significant in 6 of 12 studies (pOR: 1.70, 95% CI: 1.31-2.22); (ii) Large ulcer size: significant in 8 of 12 studies (pOR: 2.81, 95% CI: 1.98-4.00); (iii) Posterior duodenal ulcer location: significant in four of eight studies (pOR: 3.83, 95% CI: 1.38-10.66) and (iv) High lesser gastric curvature ulcer location: significant in three of eight studies (pOR: 2.86; 95% CI: 1.69-4.86). CONCLUSIONS: Major predictors for rebleeding in patients receiving endoscopic therapy are haemodynamic instability, active bleeding at endoscopy, large ulcer size, ulcer location, haemoglobin value and the need for transfusion. These risk factors may be useful for guiding clinical management in patients with PUB.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.047 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".