RAPID framework for improved access to precision oncology for lethal disease: Results from a modified multi-round delphi study
Bibliographic record
Abstract
Introduction Predictive oncology, germline technologies, and adaptive seamless trials are promising advances in the treatment of lethal cancers. Yet, access to these therapies is stymied by costly research, regulatory barriers, and structural inequalities worsened by the COVID-19 pandemic. Methods To address the need for a comprehensive strategy for rapid and more equitable access to breakthrough therapies for lethal cancers, we conducted a modified multi-round Delphi study with 70 experts in oncology, clinical trials, legal and regulatory processes, patient advocacy, ethics, drug development, and health policy in Canada, Europe, and the US. Semi-structured ethnographic interviews (n = 33) were used to identify issues and solutions that participants subsequently evaluated in a survey (n = 47). Survey and interview data were co-analyzed to refine topics for an in-person roundtable where recommendations for system change were deliberated and drafted by 26 participants. Results Participants emphasized major issues in patient access to novel therapeutics including burdens of time, cost, and transportation required to complete eligibility requirements or to participate in trials. Only 12% of respondents reported satisfaction with current research systems, with “patient access to trials” and “delays in study approval” the topmost concerns. Conclusion Experts agree that an equity-centered precision oncology communication model should be developed to improve access to adaptive seamless trials, eligibility reforms, and just-in-time trial activation. International advocacy groups are a key mobilizer of patient trust and should be involved at every stage of research and therapy approval. Our results also show that governments can promote better and faster access to life-saving therapeutics by engaging researchers and payors in an ecosystem approach that responds to the unique clinical, structural, temporal, and risk-benefit situations that patients with life-threatening cancers confront.
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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 | 0.212 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".