The Risk of Glioblastoma with TNF Inhibitors
Bibliographic record
Abstract
PURPOSE: To quantify the risk of glioblastoma (GBM) and its most aggressive form, glioblastoma multiforme (GBM-M), in patients treated with tumor necrosis factor (TNF) inhibitors. METHODS: Data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and the World Health Organization (WHO) Collaborating Centre for International Drug Monitoring were used to perform a disproportionality analysis. We computed reporting odds ratios (RORs) and corresponding 95% confidence intervals for the association between use of TNF inhibitors (infliximab, adalimumab, etanercept, certolizumab, and golimumab) and GBM or GBM-M compared to all other drugs with adverse events reported in the databases. A harmful signal was deemed for a lower limit of the 95% confidence interval above 1. RESULTS: We identified 81 cases of GBM or GBM-M with adalimumab in the U.S. FDA FAERS and 49 cases in the WHO drug monitoring database. For infliximab, 40 and 32 cases were identified in the FAERS and WHO databases, respectfully. Infliximab had the highest association with GBM (WHO: ROR = 7.41 (5.19-10.57), FAERS: ROR = 2.80 [1.89-4.15]). Adalimumab was also highly associated with GBM (WHO: ROR = 3.54 [2.58-4.89], FAERS: ROR = 1.99 [1.41-2.80]). CONCLUSION: Several TNF inhibitors appear to be more strongly associated with GBM compared to other drugs in both the FAERS and WHO databases. Large epidemiologic studies are needed to confirm these findings. Although these results do not demonstrate a cause-and-effect relationship, they warrant further investigation by well-designed epidemiologic studies.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".