Abstract WP338: Combined Coiling and Allogeneic Mesenchymal Stem/Stromal Cell Therapy Could be a More Cost-effective Alternative to Coiling Alone
Bibliographic record
Abstract
Introduction: Recurrence of intracranial aneurysms following endovascular therapy in 20% of patients remains the only major disadvantage of this treatment. For this reason, a significant amount of research has been carried out, focused on reducing the incidence of recurrence. In recent years, a variety of cell therapy modalities using fibroblasts, smooth muscle cells, endothelial progenitor cells and Mesenchymal Stem/Stromal Cells (MSCs) have been tested in animal models as a means to improve the outcome of the treatment. However, it remains unclear whether preventing recurrence using cell therapy is a more cost-effective alternative to retreating recanalized aneurysms. In this study, we have used a Markov model approach to determine efficacy thresholds at which combined coiling and cell therapy becomes a more cost-effective treatment than coiling alone. Hypothesis: Combined coiling and cell therapy will be more cost-effective than coiling alone, if it reduces the need for retreatment by 50% or more. Methods: The cell therapy was assumed to be aimed at reducing the need for retreatment. A Markov model was used to compare coiling alone with combined coiling and autologous/allogeneic cell therapy. Model inputs were mostly taken from meta-analyses. Sensitivity analysis was performed to predict efficacy thresholds that make cell therapy more cost-effective than coiling alone. Robustness of the model was assessed through further sensitivity testing focused on variables with the highest impact on the outcome. Results: Sensitivity analysis showed that coiling with autologous cell therapy becomes more cost-effective than coiling alone, if it reduces the need for retreatment by 39.9% or more. When allogeneic cell are used, a reduction of 13.3% or more in the need for retreatment is enough the make combined coiling and cell therapy more cost-effective. Conclusions: Our preliminary analysis suggests that efficacy thresholds at which combined coiling and cell therapy becomes more cost-effective than coiling alone are modest - especially for allogeneic MSC therapies. This makes combined coiling and cell therapy a viable alternative to the current standard-of-care from a cost-utility standpoint, and justifies further research and investment in the field.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".