Beyond Participation: Evaluating the Role of Patients in Designing Oncology Clinical Trials
Bibliographic record
Abstract
Historically, subject matter experts and healthcare professionals have played a pivotal role in driving oncology clinical trials. Although patients have been key participants, their deliberate and active contribution to the design and decision-making process has been limited. This scoping review aimed to examine the existing literature to scope the extent of active patient engagement in the design of oncology clinical trials and its corresponding influence on trial outcomes. We conducted a systematic search using two databases, namely MEDLINE (Ovid) and EMBASE, to identify relevant studies exploring patient engagement in cancer-related clinical research design. We identified seven studies that met the eligibility criteria. The studies highlighted the benefits of active patient involvement, such as improved recruitment strategies, and the attainment of more patient-centered trial outcomes. The influence of patient involvement varied from tangible developments like patient-friendly resources to indirect impacts like improved patient experiences and potentially higher adherence to trial intervention. The future of clinical trials should prioritize patients' values and perspectives, with regulatory bodies fostering these practices through clear guidelines. As the concept of patient centricity takes root in oncology research, the involvement of patients should evolve beyond mere participation.
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 | 0.573 | 0.748 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".