A real-world data analysis of topotecan in the FDA Adverse Event Reporting System (FAERS) database
Bibliographic record
Abstract
The objective of this study was to monitor and identify adverse events (AEs) associated with topotecan, a medication used for the treatment of solid tumors, in order to improve patient safety and guide medication usage. To assess the disproportionality of topotecan-related AEs in real-world data, four algorithms (ROR, PRR, BCPNN, and EBGM) were employed as measures to detect signals of topotecan-associated AEs. A statistical analysis was conducted using data from the FAERS database, encompassing 9,511,161 case reports from 2004Q1 to 2021Q4. Among these reports, 1,896 were identified as primary suspected (PS) AEs related to topotecan, and 155 topotecan-related adverse drug reactions (ADRs) at the preferred terms (PTs) level were selected. The occurrence of topotecan-induced ADRs was analyzed across 23 organ systems. The analysis revealed several expected ADRs, such as anemia, nausea, and vomiting, which were consistent with the drug labels. Additionally, unexpected significant ADRs associated with eye disorders at the system organ class (SOC) level were identified, indicating potential adverse effects not currently mentioned in the drug instructions. This study identified new and unexpected signals of adverse drug reactions (ADRs) related to topotecan, providing valuable insights into the relationship between ADRs and topotecan usage. The findings highlight the importance of ongoing monitoring and surveillance to detect and manage AEs effectively, ultimately improving patient safety during topotecan treatment.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.615 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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; both teacher heads agree on what is shown here.
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".