Patients’ Willingness and Perspectives Toward Chimeric Antigen Receptor T-Regulatory Cell Therapy for Inflammatory Bowel Diseases
Bibliographic record
Abstract
Background: Inflammatory bowel disease is a life-changing disease resulting from recurrent intestinal inflammation. Current therapies (eg, steroids and biologics) are associated with mild to severe side effects, and none provide a cure. Recent research has focused on genetically engineering gut-specific anti-inflammatory T-regulatory cells (CAR-Tregs) to control intestinal inflammation, a logistically and conceptually complex approach. The purpose of our study was to understand patients' willingness to try CAR-Treg given 2 hypothetical scenarios-in a clinical trial or as a new treatment. Methods: We surveyed people living with inflammatory bowel disease about their willingness to try CAR-Treg. The online survey was developed using patient focus groups and associated literature. We recruited participants through email and social media. We used descriptive and inferential statistics to analyze closed-ended questions and inductive thematic analysis to analyze open-ended follow-up questions. Results: Survey participants indicated high willingness to try CAR-Treg therapy in both a clinical trial and as a new treatment. Willingness to try was not correlated with disease state or medication history. Women were less likely than men to indicate willingness to participate in a clinical trial. Participants' reasons for being willing to try CAR-Treg therapy included the wish to change their current treatment and the calling to participate in research. Participants that were not willing to try CAR-Treg mentioned the lack of long-term data and the success of their current therapy. Conclusions: This is the first study to our knowledge to investigate patient willingness to try CAR-Treg therapy. Our results demonstrate the promise of moving this therapy into clinical practice as most patients indicated willingness to try.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".