Association Between PD-L1 Inhibitor, Tumor Site and Adverse Events of Potential Immune Etiology Within the US FDA Adverse Event Reporting System
Bibliographic record
Abstract
Objective: To analyze tumor- and treatment-related factors that might impact the development of certain adverse events (AEs) of potential immune etiology among patients receiving PD-L1 inhibitors. Methods: The FDA Adverse Event Reporting System (FAERS) was accessed, and AE reports related to the use of PD-L1 inhibitors were reviewed. Associations between treatment, tumor type and occurrence of AEs of special interest were analyzed through multivariable logistic regression analysis. Results: A total of 80,304 AE reports were included in the current analysis. Diagnosis with lung cancer was associated with a higher probability of pneumonitis; diagnosis with melanoma was associated with a higher probability of hepatitis, hypophysitis/hypopituitarism and uveitis; and diagnosis with genitourinary cancers was associated with a higher probability of nephritis, adrenal insufficiency and myocarditis. Conclusion: Within this cohort limited to AEs reported to the FAERS, there is an association between different AEs of special interest, agent(s) used and tumor(s) treated.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".