Post-Marketing Safety of Vemurafenib: A Real-World Pharmacovigilance Study of the FDA Adverse Event Reporting System
Bibliographic record
Abstract
PURPOSE: Vemurafenib received approval for treatment of BRAF V600 variation metastatic melanoma in August 2011. This study analyzed Vemurafenib-related adverse events (AEs) to detect and characterize relevant safety signals using the real-word-data through the Food and Drug Administration Adverse Event Reporting System (FAERS). METHODS: Disproportionality analyses, including the reporting odds ratio (ROR), the healthcare products regulatory agency (MHRA), the Bayesian confidence propagation neural network (BCPNN), and the multiitem gamma Poisson shrinker (MGPS) algorithms, were applied to quantify the signals of vemurafenib-related AEs. RESULTS: Out of 8,042,244 reports gathered from the FAERS, 9554 reports of vemurafenib as the 'primary suspected (PS)' AEs were recognized. Vemurafenib-induced AEs occurrence targeted 23 system organ class (SOC). A total of 138 significant disproportionality PTs was simultaneously reserved according to the four algorithms. Unexpected significant AEs such as sarcoidosis and kidney fibrosis might also occur. The median onset time of vemurafenib-related AEs was 26 days (interquartile range [IQR] 8-97 days), and most of the cases occurred within the first one and two months after vemurafenib initiation. CONCLUSION: Our study detected potential new AEs signals and might provide powerful support for clinical monitoring and risk identification of vemurafenib.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".