Development of a policy model for pediatric acute lymphoblastic leukemia to facilitate economic evaluation
Bibliographic record
Abstract
BACKGROUND: New highly effective, but expensive, immunotherapies have revolutionized the treatment of relapsed pediatric acute lymphoblastic leukemia (ALL) but their long-term clinical and economic impact is unclear. We developed the ALL Policy microsimulation model to estimate long-term clinical and economic outcomes for patients with pediatric ALL aged 0-17 in Ontario, Canada. We also illustrate the model's clinical utility through a cost-effectiveness analysis of blinatumomab in relapsed B-cell ALL. METHODS: The ALL Policy model is informed using health administrative data and chart abstracted data from Ontario, Canada, and published literature. The model estimates lifetime risk of relapse, bone marrow transplant (BMT), conditional life expectancy, quality-adjusted life years (QALYs), and total health-care costs for individuals with pediatric ALL and can be stratified by relevant clinical characteristics (eg, B-cell or T-cell lineage). Additionally, we subset the model to patients with relapsed B-cell ALL to illustrate use of the model in estimating the cost-effectiveness of blinatumomab vs standard chemotherapy. RESULTS: Simulated pediatric ALL patients diagnosed from 2002 to 2012 had a projected conditional life expectancy of 64.90 years. The lifetime risk of BMT was estimated at 12.5%. Lifetime health-care costs were $244 433 Canadian Dollars (CAD) (95% confidence interval = $213 314 to $303 430). Treatment with blinatumomab compared to standard chemotherapy post-relapse was estimated to result in 1.08 additional QALYs and an additional cost of $59 410 CAD (incremental cost-effectiveness ratio of $54 885/QALY). CONCLUSION: The ALL policy model can serve as a modeling foundation for timely economic evaluation. Introduction of blinatumomab in relapsed B-cell ALL may be a cost-effective strategy.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".