Challenges of Conducting Value Assessment for Comprehensive Genomic Profiling
Bibliographic record
Abstract
OBJECTIVES: Clinical practice is shifting toward an era of precision medicine. The use of comprehensive genomic profiling (CGP) in oncology has broad potential as a universal companion diagnostic for targeted therapies which may significantly improve health outcomes while using healthcare resources more efficiently. Given the nature of this technology, assessing the value of CGP presents unique challenges. METHODS: This paper draws on evidence from the academic and policy literature in oncology, as well as stakeholder interviews (health economists, payers, clinicians, and public policy officials) in countries using incremental cost-effectiveness ratios (ICER) as part of health technology assessment (HTA). RESULTS: The degree to which CGP is subject to a value assessment varies significantly across healthcare systems. Current HTA processes focus on evaluating diagnostic testing through co-dependent assessment of diagnostic testing and associated therapeutic interventions. Diagnostic tests with multiple associated therapeutic interventions are rapidly evolving and poorly unsuited to current HTA approaches. Moreover, HTA approaches are limited in their ability to consider broader systemic benefits of the expanded diagnostic capabilities and enhanced opportunities for clinical trial participation offered by CGP. CONCLUSIONS: The assessment of the overall value of CGP is limited by the current models of HTA. This paper suggests policy proposals for value assessment and funding reforms to help broaden patient access to CGP. These include investing in genomic testing infrastructure; decoupling the assessment of the value of CGP testing to identifying predetermined therapeutic interventions; tailoring evaluation methodology; and developing approaches to collecting evidence of clinical, healthcare system and societal benefit.
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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 | 0.566 | 0.763 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".