How life-cycle real-world evidence can bridge evidentiary gaps in precision oncology
Bibliographic record
Abstract
Precision oncology uses omics-based diagnostic technologies to inform histology-agnostic cancer treatment. To date, health system implementation remains limited owing to high uncertainty in regulatory and reimbursement evidence submissions. In this perspective, we describe a life-cycle approach to the evaluation of precision oncology technologies that addresses evidentiary uncertainty and is grounded in real-world evidence (RWE) derived using data routinely collected by healthcare systems. We consider the role for RWE in international regulatory and reimbursement decision-making, review common biases for observational precision oncology evaluations, make specific recommendations for RWE study design and analysis, and specify healthcare system requirements for data collection. We then explore how decision-grade real-world data can support the generation of decision-grade RWE, ultimately enabling real-world life-cycle assessment for precision oncology.
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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 | 0.329 | 0.688 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".