<i>Helicobacter pylori</i> eradication is superior to ulcer healing with or without maintenance therapy to prevent further ulcer haemorrhage
Bibliographic record
Abstract
BACKGROUND: Helicobacter pylori eradication decreases ulcer recurrence and should prevent recurrent ulcer haemorrhage. AIM: By meta-analysis, to compare treatment of H. pylori infection with other approaches to prevent recurrent ulcer haemorrhage and, by cost minimization analysis, to determine the least costly strategy. METHODS: We searched for randomized, controlled trials comparing treatment of H. pylori infection with ulcer healing alone or with maintenance therapy in preventing recurrent ulcer haemorrhage. We calculated the relative and absolute risk reductions and numbers needed to treat. RESULTS: Treatment of H. pylori infection decreased recurrent bleeding by 17% (numbers needed to treat=6) compared with ulcer healing treatment alone. Compared with ulcer healing treatment followed by maintenance therapy, recurrent bleeding was decreased by 4% (numbers needed to treat=25). Decision model-based cost minimization analysis demonstrated that treatment of H. pylori infection was the least costly strategy unless the incidence of complicated recurrences after treatment was over 6%, or the cost of confirming eradication was over $741. CONCLUSIONS: Treatment of H. pylori infection is superior to ulcer healing treatment with or without maintenance therapy in preventing recurrent ulcer haemorrhage. All patients with ulcer bleeding should be tested for H. pylori infection and appropriately treated if positive.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".