A Novel Composite Endpoint to Evaluate the Gastrointestinal (GI) Effects of Nonsteroidal Antiinflammatory Drugs Through the Entire GI Tract
Bibliographic record
Abstract
OBJECTIVE: Nonsteroidal antiinflammatory drugs (NSAID) not only cause damage to the upper gastrointestinal (GI) tract but also affect the lower GI tract. To date, there is no endpoint that evaluates serious GI events in the entire GI tract. The objective of this report is to introduce a novel composite endpoint that measures damage to the entire GI tract - clinically significant upper and lower GI events (CSULGIE) - in patients with NSAID-induced GI damage. METHODS: We reviewed the data from largescale, multicenter, randomized, clinical trials on lower GI toxicity associated with NSAID use. The rationale for using CSULGIE as a primary endpoint in 2 ongoing trials - the Celecoxib vs Omeprazole and Diclofenac for At-risk Osteoarthritis (OA) and Rheumatoid Arthritis (RA) Patients (CONDOR) trial and the Gastrointestinal Randomized Events and Safety Open-Label NSAID Study (GI-REASONS) - is also discussed. RESULTS: Previous randomized trials focused primarily on damage to the upper GI tract and often neglected the lower GI tract. The CSULGIE endpoint extends the traditional "perforation, obstruction, and bleeding" assessment of upper GI complications by including events in the lower GI tract (small/large bowel) such as perforation, bleeding, and clinically significant anemia. CONCLUSION: By providing clinicians with a new, descriptive language for adverse events through the entire GI tract, the CSULGIE endpoint has the potential to become a standard tool for evaluating the GI effects of a range of therapies.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".