Gastrointestinal perforation associated with novel antineoplastic agents: A real-world study based on the FDA Adverse Event Reporting System
Bibliographic record
Abstract
Purpose: Gastrointestinal perforation (GIP) is a fatal adverse event (AE). The AE of GIP induced by novel antineoplastic agents has attracted attention recently. We aimed to explore the AE signals of GIP related to novel antineoplastic agents comprehensively based on the FDA Adverse Event Reporting System (FAERS). Methods: The FAERS database containing 71 quarters of records was used for analysis. Reporting odds ratio (ROR), information component (IC), and empirical Bayesian geometric mean (EBGM) were utilized to evaluate the signals of GIP associated with novel antineoplastic drugs. Standardization of drug names was by employing MedEx-UIMA software and Python. Data analysis and visualization were performed using MySQL Workbench and R software. Results: After cleaning and handling the data, 5226 GIP cases were identified that were associated with new antineoplastic medications, where these agents were the main suspected contributors. A total of 37 novel antineoplastic drugs were detected with signals of GIP for ROR and IC. Only 22 drugs showed statistically significant signals for EBGM. We found the GIP signals of 22 novel antineoplastic drugs overlapped for the 3 indicators, including anti-vascular endothelial growth factor/vascular endothelial growth factor receptor, anti-endothelial growth factor receptor, immune checkpoint inhibitors, and so on. Conclusion: The potential risk of GIP associated with several novel antineoplastic agents was identified through data mining, which provided valuable information on the safety risks associated with GIP among these drugs. The potential threat of GIP should be recognized and managed properly when using these novel antineoplastic agents.
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 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".