Commentary: what is the optimal <scp>PPI</scp> dosing following endoscopic haemostasis in acute ulcer bleeding?
Bibliographic record
Abstract
We commend Chen et al. on a well-reported randomised controlled trial (RCT) addressing a persistent controversy in the management of ulcer bleeding.1 The trial avoided two main limitations of most previous RCTs: the authors excluded patients exhibiting low-risk endoscopic stigmata, and adopted efficacious endoscopic therapy.2, 3 Unfortunately methodological shortcomings remain. Was the method of blinding adequate, and how was it implemented? This aspect is critical when assessing risk of bias and RCT validity. The comparator dose used was very low but may be adequate in this population.4 The main limitation is the lack of statistical power. Indeed, the observed difference in re-bleeding rates of 1% carries wide 95% confidence intervals (–5.6% to +7.6%), compatible with clinically important differences in either direction. A recent Cochrane systematic review (13 studies, 1727 patients), comparing ‘high-dose’ PPI to other regimens, found no statistically significant differences in clinical outcomes.5 However, as the overall quality of evidence ranged from ‘very low’ to ‘low’, mainly due to high risk of bias and imprecision, even these better powered summary results are inconclusive, and do not provide evidence for therapeutic equivalence.2, 3 Such considerations motivated the recent recommendations by an international consensus group, favouring a high-dose intravenous PPI regimen in patients with high-risk endoscopic stigmata after endoscopic haemostatic therapy until better quality data come to light,6 especially when considering the risk-benefit ratio of PPIs for this indication, and the demonstrated cost-effectiveness of the high-dose approach compared to not using a PPI acutely.7-9 The suggestion that H. pylori presence may be associated with decreased re-bleeding is biologically plausible and requires confirmation. In conclusion, this study unfortunately does not provide decisive data, but may be useful when included in future meta-analyses to determine the optimal PPI dosing when managing patients with ulcer bleeding. Declaration of personal interests: Alan Barkun is a consultant for AstraZeneca, Takeda Canada, Boston Scientific Inc. and Olympus Canada, and received research funding from Boston Scientific Inc. and Cook. Grigorios I. Leontiadis has received research funding from AstraZeneca. Declaration of funding interests: None.
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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 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.010 | 0.002 |
| Research integrity | 0.044 | 0.023 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".