Economic impact of genomic diagnostics for intermediate‐risk acute myeloid leukaemia
Bibliographic record
Abstract
Acute Myeloid Leukaemia (AML) is a rare but serious group of diseases that require critical decision-making for curative treatment. Over the past decade, scientific discovery has revealed dozens of prognostic gene mutations for AML while sequencing costs have plummeted. In this study, we compared the cost-effectiveness of multigene integrative analysis (genomic analysis) with the standard molecular testing currently used for diagnosis of intermediate-risk AML. We used a decision analytic model with data for costs and outcomes from British Columbia, Canada, to assess the long-term (10-year) economic impacts. Our results suggest that genomic analysis would result in a 26% increase in the use of first-remission allogeneic stem cell transplantation. The resulting treatment decisions and downstream effects would come at an additional cost of $12 556 [2013 Canadian dollars (CAD)] per person and the incremental cost-effectiveness ratio would be $49 493 per quality-adjusted life-year gained. Cost-effectiveness was dependent on quality of life during the long-term (5-10) years of survival, relapse rates following first-remission chemotherapy and the upfront cost of transplantation. Non-relapse mortality rates, short-term quality of life and the cost of genomic sequencing had only minor impacts. Further research on post-remission outcomes can lead to improvements in the cost-effectiveness of curative treatments for AML.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".