A perspective on life-cycle health technology assessment and real-world evidence for precision oncology in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health technology assessment (HTA) can be used to make healthcare systems more equitable and efficient. Advances in precision oncology are challenging conventional thinking about HTA. Precision oncology advances are rapid, involve small patient groups, and are frequently evaluated without a randomized comparison group. In light of these challenges, mechanisms to manage precision oncology uncertainties are critical. We propose a life-cycle HTA framework and outline supporting criteria to manage uncertainties based on real world data collected from learning healthcare systems. If appropriately designed, we argue that life-cycle HTA is the driver of real world evidence generation and furthers our understanding of comparative effectiveness and value. We conclude that life-cycle HTA deliberation processes must be embedded into healthcare systems for an agile response to the constantly changing landscape of precision oncology innovation. We encourage further research outlining the core requirements, infrastructure, and checklists needed to achieve the goal of learning healthcare supporting life-cycle HTA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it