Beyond pain relief: the thrombosis threat of celecoxib
Bibliographic record
Abstract
Background: There are still some points of controversy regarding the adverse events associated with celecoxib use, particularly in terms of thrombosis. Objectives: To explore the relationship between celecoxib and thrombosis in the real world and to investigate the causality that exists. Design: We conducted pharmacovigilance analysis on spontaneously reported adverse events to evaluate the association between celecoxib and thrombotic events. In addition, Mendelian randomization studies of drug targets were used to explore the causal relationship between them. Methods: This study used the data from the United States Food and Drug Administration Adverse Event Reporting System (FAERS), the Japanese Adverse Drug Event Report database, and the Canada Vigilance Adverse Reaction (CVAR) for pharmacovigilance analysis. Among these, the Report Odds Ratio, Proportional Reporting Ratio, Information Component, and Empirical Bayesian Geometric Mean were used to determine the strength of adverse event signals. In addition, the Weibull shape parameter test was used in this study to investigate the trend of adverse events. Mendelian randomization was used to explore the causal link between celecoxib and deep vein thrombosis. Results: Pharmacovigilance signals indicated that celecoxib was associated with an increased risk of thrombosis, with deep vein thrombosis demonstrating positive signals in all three populations. In addition, Mendelian randomization analyses provided evidence to support a causal relationship between celecoxib and deep vein thrombosis and clarified that carbonic anhydrase 2, a target protein of celecoxib, is causally linked to deep vein thrombosis. Conclusion: The use of celecoxib leads to an increased risk of thrombosis and suggests a causal relationship.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".