Challenges and Opportunities in Adapting Clinical Trial Design for Immunotherapies
Bibliographic record
Abstract
Abstract Immunotherapy adds an exciting new dimension to the treatment of cancer, joining other approaches as a key pillar in the oncotherapeutics armamentarium. Immuno-oncology agents harbor unique mechanisms of antitumor activity by leveraging the host immune system, which may result in response patterns, resistance kinetics, and toxicity profiles that differ from other systemic therapies. These features have led to many discussions on ways to optimally integrate immunotherapy into cancer clinical trials. This overview provides an introduction to the four CCR Focus articles that ensue, with special thoughts paid to clinical trial endpoints, biomarker development and validation, combination strategies, and limitations that arise with increasing use of these agents. In addition, this overview examines design concepts that may be applied to invigorate clinical trials and to maximize their impact in the immuno-oncology era. Clin Cancer Res; 23(17); 4950–8. ©2017 AACR. See all articles in this CCR Focus section, “Clinical Trial Design Considerations in the Immuno-oncology Era.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.280 | 0.431 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.018 | 0.048 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".