Partnering with patients to get better outcomes with chimeric antigen receptor T-cell therapy: towards engagement of patients in early phase trials
Bibliographic record
Abstract
AIM: Though patient engagement in clinical research is growing, recent reports suggest few clinical trials report on such activities. To address this gap, we describe our approach to patient engagement in the development of a clinical trial protocol to assess a new immunotherapy for blood cancer (chimeric antigen receptor T-cell therapy, CAR-T cell therapy). METHODS: Our team developed a clinical trial protocol by working with patient partners from inception. Two patient partners with lived blood cancer experience were identified through referrals from our team's professional network and patient organization contacts. Our patient partners were onboarded to the team and engaged in several studies conducted to develop the clinical trial protocol, including a systematic review of the existing literature on the therapy, patient interviews and a survey to obtain perspectives on barriers and enablers to participating in the trial, an early economic analysis, and a retrospective cohort study. RESULTS: Engaging patient partners enhanced our research in ways that would not have otherwise occurred. By selecting patient important outcomes for data collection, our partners helped flag that quality of life and health utility measures have not been reported in previous CAR-T cell therapy trials for blood cancer. Our partners also co-developed a non-technical summary of the systematic review that summarized results in an accessible manner. Our patient partners reviewed interview and survey questions, to improve the language and appropriateness; provided recruitment suggestions; and provided a patient perspective on the results, thereby confirming the importance of findings. Input was also obtained on costs for the early economic analysis. Our patient partners identified costs that may be a burden to both patients and caregivers during a trial and helped to confirm that the overall structure of the economic model reflected the patient care pathway. Our patient partners also shared their diagnosis and treatment stories, which helped to provide the research team with insight into this experience. CONCLUSIONS: Contributions by our patient partners were invaluable to each component study, as well as the overall development of the trial protocol. We plan to use this approach in the future in order to meaningfully engage patients in the development of other clinical trials; we also hope that by reporting our methods this will help other research teams to do the same. TRIAL REGISTRATION: Affiliated with the development of NCT03765177.
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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 | 0.589 | 0.553 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".