An initial health economic evaluation of pharmacogenomic testing in patients treated for childhood cancer with anthracyclines
Bibliographic record
Abstract
BACKGROUND: Anthracyclines are a class of highly effective chemotherapeutic drugs commonly used to treat cancer patients. Anthracyclines, however, are associated with the development of serious adverse reactions, including anthracycline-induced cardiotoxicity (ACT). It is not possible, within current practice, to accurately individualize treatment to minimize risk. PROCEDURE: Recently, genetic variants have been associated with the risk of ACT in children. Building on these findings and the related genetic test, a predictive model was developed which classifies pediatric patients by their risk of developing ACT. We assessed the value of this ACT-predictive risk classification in addressing ACT. RESULTS: With current care, the estimated average lifetime cost of ACT is $8,667 per anthracycline-treated patient and approximately 7% of patients are expected to die from ACT. The projected impact of the information from the new predictive model is a 17% reduction in the risk of mortality from ACT and savings of about 6%: lives saved and lower costs. CONCLUSION: The newly identified genetic variants associated with the risk of ACT provide information that allows a more reliable prediction of the risk of ACT for a given patient and can be obtained at a very moderate cost, which is expected to lead to meaningful progress in reducing harm and costs associated with ACT.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.005 | 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".