Reflections on the opportunities and challenges of applying experience‐based co‐design (EBCD) to phase 1 clinical trials in oncology
Bibliographic record
Abstract
BACKGROUND: Experience-Based Co-Design (EBCD) is a multi-stage participatory action research process which was developed originally to increase patient involvement in service improvement initiatives. This viewpoint article serves as a reflection on the researchers' experiences, focusing on the application and feasibility of participatory approaches, particularly co-design, in the specific context of early-phase clinical trials. METHODS: We reflect on the opportunities and challenges of applying EBCD in a new context of early-phase clinical trials in oncology where experimental treatments are increasingly perceived as a therapeutic option and, in certain instances, their efficacy may lead to accelerated approval facilitating a swifter integration into standard care. RESULTS: We propose that the opportunity of applying EBCD in such trials lies in improving the delivery of person-centered care, care coordination, and support during the transition from experimental to standard care. Three potential challenges when applying EBCD in early-phase clinical trials are discussed related to: the need for standardization in trial processes; planning EBCD in a context of high uncertainty; and vulnerability of patient populations. CONCLUSION: Integrating EBCD into early-phase oncology trials presents an opportunity to enhance person-centered care and can lead to simultaneous improvements in care processes and therapeutic development. PATIENT OR PUBLIC CONTRIBUTION: This article has been developed with the collaboration of a patient partner who serves on the advisory board of our ongoing EBCD study in early clinical trials.
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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 | 0.470 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.058 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.010 | 0.035 |
| Research integrity | 0.022 | 0.047 |
| 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".