A cost effectiveness analysis of thiopurine methyltransferase testing for guiding 6‐mercaptopurine dosing in children with acute lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: An increased understanding of the genetic basis of disease creates a demand for personalized medicine and more genetic testing for diagnosis and treatment. The objective was to assess the incremental cost-effectiveness per life-month gained of thiopurine methyltransferase (TPMT) genotyping to guide doses of 6-mercaptopurine (6-MP) in children with acute lymphoblastic leukemia (ALL) compared to enzymatic testing and standard weight-based dosing. PROCEDURE: A cost-effectiveness analysis was conducted from a health care system perspective comparing costs and consequences over 3 months. Decision analysis was used to evaluate the impact of TPMT tests on preventing myelosuppression and improving survival in ALL patients receiving 6-MP. Direct medical costs included laboratory tests, medications, physician services, pharmacy and inpatient care. Probabilities were derived from published evidence. Survival was measured in life-months. The robustness of the results to variable uncertainty was tested in one-way sensitivity analyses. Probabilistic sensitivity analysis examined the impact of parameter uncertainty and generated confidence intervals around point estimates. RESULTS: Neither of the testing interventions showed a benefit in survival compared to weight-based dosing. Both test strategies were more costly compared to weight-based dosing. Incremental costs per child (95% confidence interval) were $277 ($112, $442) and $298 ($392, $421) for the genotyping and phenotyping strategies, respectively, compared to weight-based dosing. CONCLUSIONS: The present analysis suggests that screening for TPMT mutations using either genotype or enzymatic laboratory tests prior to the administration of 6-MP in pediatric ALL patients is not cost-effective.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".