Rare and Insidious Toxicities from New Combination Therapies in Metastatic Renal Cell Cancer: Lessons Learned from Real-Practice
Bibliographic record
Abstract
The advent of immune checkpoint inhibitors in combination with multitarget tyrosine kinase inhibitors has become a standard first-line treatment for metastatic renal cell cancer. Along with survival improvement, new toxicities have emerged. Such adverse events are still complex to be managed and some of them are rare and could be insidious or even fatal. Medical oncologists dispose of guidelines about the management of toxicities from immune checkpoint inhibitors but not for combinations. Therefore, it is still difficult to properly attribute and manage additive or overlapping adverse events. We report two clinical cases regarding rare treatment-related endocrine toxicities-hypophysitis and thyroiditis-with particular focus on their management. To this purpose, immune checkpoint-related toxicities guidelines represent the starting point. However, their implementation with additional measures is needed, considering the increasing complexity of current clinical scenarios. The goal is to correctly recognize adverse events and address side effects, so as not to discontinue effective treatments. We, therefore, aim at discussing the points of proper management of toxicities and individuating potential areas of improvement.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".