Stem cells and beta cell replacement therapy: a prospective health technology assessment study
Bibliographic record
Abstract
BACKGROUND: Although current beta cell replacement therapy is effective in stabilizing glycemic control in highly selected patients with refractory type 1 diabetes, many hurdles are inherent to this and other donor-based transplantation methods. One solution could be moving to stem cell-derived transplant tissue. This study investigates a novel stem cell-derived graft and implant technology and explores the circumstances of its cost-effectiveness compared to intensive insulin therapy. METHODS: We used a manufacturing optimization model based on work by Simaria et al. to model cost of the stem cell-based transplant doses and integrated its results into a cost-effectiveness model of diabetes treatments. The disease model simulated marginal differences in clinical effects and costs between the new technology and our comparator intensive insulin therapy. The form of beta cell replacement therapy was as a series of retrievable subcutaneous implant devices which protect the enclosed pancreatic progenitors cells from the immune system. This approach was presumed to be as effective as state of the art islet transplantation, aside from immunosuppression drawbacks. We investigated two different cell culture methods and several production and delivery scenarios. RESULTS: We found the likely range of treatment costs for this form of graft tissue for beta cell replacement therapy. Additionally our results show this technology could be cost-effective compared to intensive insulin therapy, at a willingness-to-pay threshold of $100,000 per quality-adjusted life year. However, results also indicate that mass production has by far the best chance of providing affordable graft tissue, while overall there seems to be considerable room for cost reductions. CONCLUSIONS: Such a technology can improve treatment access and quality of life for patients through increased graft supply and protection. Stem cell-based implants can be a feasible way of treating a wide range of patients with type 1 diabetes.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".