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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".