Real-world analyses of therapy discontinuation of checkpoint inhibitors in metastatic melanoma patients
Bibliographic record
Abstract
The 'real-world' patient population of metastatic melanoma is not fully represented in clinical trials investigating checkpoint inhibitors. We described therapy discontinuation in an unselected population-based cohort of adults with metastatic melanoma who started therapy with pembrolizumab, nivolumab, or nivolumab/ipilimumab from January 2015 to August 2017. Therapy discontinuation was defined as a gap between doses beyond 120 days, and/or initiation of another cancer therapy. We estimated drug-specific rate ratios for therapy discontinuation adjusted for age, sex, comorbidities, health care use, and past cancer therapies. We included 876 metastatic melanoma patients initiating pembrolizumab (44.3%), nivolumab/ipilimumab (31.2%), and nivolumab (24.5%). At 12 months of follow-up, the probabilities of therapy discontinuation were 49.9% (95% confidence interval, CI 43.6-56.5) for pembrolizumab, 58.8% (95% CI 50.5-67.3) for nivolumab, and 59.2% (95% CI 51.7-66.8) for nivolumab/ipilimumab. Stratified analyses based on prior cancer therapy, brain metastases at baseline, and sex showed similar trends. In multivariable analyses, compared with pembrolizumab, patients starting nivolumab (rate ratio 1.38, 95% CI 1.08-1.77) or nivolumab/ipilimumab (rate ratio 1.30, 95% CI 1.02-1.65) were more likely to discontinue therapy. Our findings indicate frequent discontinuations of checkpoint inhibitors at one year. The lower discontinuation associated with pembrolizumab should be confirmed in further 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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".