Perceptions of the Benefits and Risks of Novel Therapies for Type 1 Diabetes: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: Novel therapies, including disease-modifying and cell replacement therapies, may preserve or replace beta cells in people with type 1 diabetes. This study sought to understand how people living with type 1 diabetes or caring for someone with type 1 diabetes perceive the benefits and risks of novel therapies. METHODS: Semistructured qualitative interviews were conducted with 26 participants in the United States: four adolescents and 12 adults with type 1 diabetes, and 10 caregivers of children with type 1 diabetes. A description of the benefits and risks of disease-modifying and cell replacement therapies, developed with a steering committee of patients and clinicians, was presented during interviews to facilitate discussion among people living with type 1 diabetes and caregivers. A qualitative directed content analysis was conducted. RESULTS: Participants reported that type 1 diabetes and insulin therapy regimens impacted many life areas, with some participants reporting diabetes burnout. Most participants expressed that they would have considered trying disease-modifying therapies, most frequently citing perceived benefits such as reduced insulin reliance and an extended post-diagnosis "honeymoon period" providing time to prepare for life with diabetes. Cancer risk was the most frequently reported risk of concern for disease-modifying therapies. All participants expressed willingness to consider cell replacement therapies, with insulin independence and restored pancreatic function perceived to offer greater normalcy and freedom from the constant demands of diabetes. Participants reported concerns about the use of immunosuppressants and the risks and drawbacks of the cell replacement surgical procedure. CONCLUSIONS: Despite concerns about the risks and drawbacks of novel therapies, most participants reported that they would consider trying disease-modifying and cell replacement therapies. There is no substitute for consulting people living with or caring for someone with type 1 diabetes when considering new therapies with novel risks and benefits.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".