Life-cycle health technology assessment for precision oncology.
Bibliographic record
Abstract
e18704 Background: Rapid advances in precision oncology challenge timely and sustainable reimbursement decisions. Life-cycle health technology assessment (LC-HTA) can enable conditional patient access to promising precision oncology innovations alongside evidence development. Our objective was to create a life-cycle evaluative framework, called PRecision oncology Evidence Development in Cancer Treatment (PREDiCT). Methods: Through an iterative, health system and stakeholder-informed approach, we designed our LC-HTA framework. Elements supporting data and evidence generation were subsequently implemented within British Columbia, Canada’s provincial cancer control system. Our development, refinement, and pilot implementation process included a structured literature review, multi-disciplinary international expert consultation, a formal gap assessment, and a series of pan-Canadian inter-disciplinary stakeholder workshops to refine framework elements. Results: We engaged n = 15 pan-Canadian and international stakeholders to co-develop the LC-HTA framework. Defining framework components include: (a) managed access that defines the time horizon and pricing conditions of real-world healthcare system trialing; (b) collection of core data elements required to enable economic evaluation of precision oncology using real world data; (c) externally leveraged real world data and evidence generation to determine comparative effectiveness, cost-effectiveness, and the value of conducting additional research; and (d) data interpretation updating decisions, including investment, continued evaluation, or disinvestment from managed access. Key to the success of early framework implementation is the expansion of infrastructure to enable routine collection and linkage of genomic sequencing and cancer treatment data, patient quality of life and clinical outcomes, as well as health resource use spanning the diagnostic, treatment, and follow up trajectory. Conclusions: Sustainable integration of precision oncology requires the design and implementation of learning healthcare systems (LHS) that integrate genomic data with other health information. LC-HTA moves beyond static estimates of clinical and cost-effectiveness to continuously generate evidence that reduces evidentiary uncertainty and supports life-cycle decisions. We are embarking on a PREDiCT pilot to implement the framework in real-time to demonstrate the ability of real-world data to support life cycle evaluation.
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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 | 0.086 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.102 | 0.017 |
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".