Interventions to improve access to clinical trials in urologic oncology
Bibliographic record
Abstract
INTRODUCTION: Most cancer patients are never enrolled in clinical trials, resulting in missed potential therapeutic benefits to patients and barriers to drug development and approval. With a focus on urologic oncology clinical trials, we reviewed the current literature on barriers to accrual and present effective interventions to overcome these barriers. METHODS: PubMed was searched for articles regarding physician referral and patient accrual to clinical trials in urologic oncology from January 2000 through June 2021. Studies were included if they were in English, related to clinical trial utilization or patient accrual in urologic oncology, peer-reviewed, primary research, survey, or systematic review, and pertained to clinical trials in the U.S. Major overlapping themes related to barriers to accrual and effective interventions were identified. RESULTS: Thirty-six studies met our inclusion criteria. Barriers fall into three categories: 1) provider; 2) patient; or 3) structural. Provider barriers include issues such as poor funding, logistical challenges, and time constraints. Patient barriers include cost, distrust of medical institutions, and lack of knowledge regarding ongoing studies. Structural barriers include lack of time and resources in community settings and difficulty with physician referrals. Effective strategies identified include increasing provider referrals through continuing education and referral pathways, increasing patient education through patient-centered marketing material, and decreasing structural barriers through patient navigation programs and community partnerships. CONCLUSIONS: We identified barriers and potential multipronged strategies targeted at patients, providers, and practices to increase clinical trial enrollment. We hope these strategies will benefit patients and providers and facilitate research development.
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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 | 0.118 | 0.381 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".