Impact of the development of immune related adverse events in metastatic melanoma treated with PD -1 inhibitors
Bibliographic record
Abstract
Some clinical trials have described improved outcomes in patients who develop immune-related adverse events (irAEs) while receiving immune checkpoint inhibitors for advanced melanoma. It is unknown if this effect would be seen in a real-world population. This is a single-center retrospective analysis of all patients receiving single-agent PD-1 inhibitor for unresectable stage III or stage IV melanoma between 2012 and 2018. The majority of patients had cutaneous melanoma and were elderly (put in median and range). Totally 33.3% were BRAF mutated and 66.7% had PD-1 inhibitor as first-line treatment for metastatic disease. Also, 22% of patients had brain metastases at presentation. Of the 87 patients included in this analysis, 48 (55%) developed at least one irAE. Dermatologic toxicities were the most common irAE. The median time to develop any irAE was 12 weeks. Only one patient died of immune-related toxicity. Overall survival in the population of patients that had an irAE was significantly greater than those that did not have any toxicity (21.1 vs. 7.5 months; P < 0.001). The development of endocrine toxicity had the strongest correlation with survival as did patient with grade 1 (NCI V.5) toxicity. The development of multiple toxicities did not correlate with survival. In patients with multiple toxicities, the type of irAE that presented initially did not impact the outcome. These findings add to the growing body of literature suggesting an association between irAEs and immune-checkpoint inhibitor efficacy while suggesting that this benefit may depend on the type of toxicity and severity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".