Generalizability of clinical trials in metastatic melanoma.
Bibliographic record
Abstract
e21004 Background: Results from clinical trials are not always generalizable to real world patients (pts). Clinicians may not adhere to the studies’ original inclusion/exclusion criteria because of differences in clinical circumstances or institutional variations in treatment policies. Our aims were to determine the pattern in which melanoma trial findings are applied in a population-based setting and to describe the treatment outcomes of these pts. Methods: We focused on melanoma as several novel therapies have been recently introduced. Pts with unresectable disease from 2013 to 2014 and referred to any 1 of 6 cancer centers in British Columbia were reviewed. Based on eligibility criteria as described in registration trials of vemurafenib (Vem) and ipilimumab (Ipi), we classified pts into trial eligible (TE) and ineligible (TI) and those treated and untreated with these agents. During the study period, Vem was approved for 1st line use in BRAF mutant pts and Ipi was funded for 2nd line use. Results: We identified 185 pts with known BRAF status for the Vem analysis and 114 pts for the Ipi analysis: median ages 64 and 59 years, 57% and 61% men, and 89% and 88% ECOG 0-1, respectively. For Vem, BRAF wild type was the most common reason for being TI. Of the remaining 86 BRAF mutant pts, 59 (69%) were considered TE of whom 51 (86%) received treatment. For Ipi, poor ECOG including rapidly progressive disease was a prevalent cause of being TI. In the Ipi cohort, 96 (84%) cases were deemed TE of whom 63 (66%) received therapy. Factors most frequently associated with non-treatment in both Vem and Ipi TE pts included comorbidities (41%), pt refusal (23%), and toxicities from prior treatments (14%). Compared to TI pts as well as those considered TE but did not receive treatment, pts deemed TE and received treatment achieved the best survival (adjusted HR 0.53, 95% CI 0.28-1.00 for Vem and adjusted HR 0.33, 95% CI 0.13-0.83 for Ipi). Conclusions: There was favorable uptake of new melanoma treatments, but a fair number of pts were considered TI, suggesting the need for trials with better external validity. Non-use of novel agents in TE pts was due to physician factors, pt preferences, and concerns about toxicities, highlighting areas that limit real world effectiveness of new melanoma drugs.
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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 | 0.426 | 0.688 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".