Meta‐analysis: incidence of endoscopic gastric and duodenal ulcers in placebo arms of randomized placebo‐controlled NSAID trials
Bibliographic record
Abstract
BACKGROUND: The safety of NSAIDs is often evaluated by comparison with placebo in clinical trials. AIM: To investigate the incidence of gastric and duodenal ulcers (GDU) in placebo arms in NSAID trials over the last three decades. METHODS: Randomized placebo-controlled trials of oral NSAIDs from 1975 to 2006 were systematically reviewed. The pooled incidence of GDU in placebo arms was calculated and compared. Meta-regression was used to identify risk factors related to the incidence of the placebo ulcer at the study level. RESULTS: Thirty-six studies met inclusion criteria (duration of 6.5 days to 24 weeks). In total, 3.29% GDUs were reported in 36 placebo arms. The incidence of GDU in placebo arms was 0, 4.20% and 3.03% in the studies from 1975-1989, 1990-1999 and 2000-2006 respectively (P > 0.05). Eligible subjects with previous GI events and eligible subjects on co-therapy with low-lose aspirin/corticosteroids were associated with the increase in placebo ulcer incidence after adjusting for other factors. CONCLUSIONS: The incidence of GDU in placebo arms has not changed significantly over the last three decades, although has decreased in the past 10 years. Studies show that previous GI events and co-therapy with low-dose aspirin/corticosteroids were associated with increasing GDU in placebo arms.
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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 | 0.035 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".