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Meta‐analysis: incidence of endoscopic gastric and duodenal ulcers in placebo arms of randomized placebo‐controlled NSAID trials

2009· review· en· W1970002505 on OpenAlexaff
Youyong Yuan, C. Wang, Yuhong Yuan, Richard H. Hunt

Bibliographic record

VenueAlimentary Pharmacology & Therapeutics · 2009
Typereview
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPlaceboMedicineIncidence (geometry)AspirinInternal medicineClinical trialRandomized controlled trialGastroenterologySurgeryPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.107
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0230.004
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.100
GPT teacher head0.404
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2009
Admission routes1
Has abstractyes

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