What Is Ailing Oncology Clinical Trials? Can We Fix Them?
Bibliographic record
Abstract
Evidence from phase three clinical trials helps shape clinical practice. However, a very small minority of patients with cancer participate in clinical trials and many trials are not completed on time due to slow accrual. Issues with restrictive eligibility criteria can severely limit the patients who can access trials, without any convincing evidence that these restrictions impact patient safety. Similarly, regulatory, organizational, and institutional hurdles can delay trial activation, ultimately making some studies irrelevant. Additional issues during trial conduct (e.g., mandatory in-person visits, central confirmation of standard biomarkers, and inflexible drug dosage modification) contribute to making trials non-patient-centric. These real-life observations from experienced clinical trialists can seem nonsensical to investigators and patients alike, who are trying to bring effective drugs to patients with cancer. In this review, we delve into these issues in detail, and discuss potential solutions to make clinical trials more accessible to patients.
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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 | 0.168 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.018 | 0.037 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".