Patient-Centered Cancer Drug Development: Clinical Trials, Regulatory Approval, and Value Assessment
Bibliographic record
Abstract
Historically, patient experience, including symptomatic toxicities, physical function, and disease-related symptoms during treatment or their perspectives on clinical trials, has played a secondary role in cancer drug development. Regulatory criteria for drug approval require that drugs are safe and effective, and almost all drug approvals have been based only on efficacy endpoints rather than on quality-of-life (QoL) assessments. In contrast to Europe, information regarding the impact of drugs on patients' QoL is rarely included in oncology drug labeling in the United States. Until recently, patient input and preferences have not been incorporated into the design and conduct of clinical trials. In recent years, a more in-depth understanding of cancer biology, as well as regulatory changes focused on expediting cancer drug development and approval, has allowed earlier access to novel therapeutic agents. Understanding the implications of these expedited programs is important for oncologists and patients, given the rapid expansion of these programs. In this article, we provide an overview of the role of QoL in the regulatory drug-approval process, key issues regarding trial participation from the patient perspective, and the implications of key expedited approval programs that are increasingly being used by regulatory bodies for cancer care.
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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 | 0.148 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".