ADAPTIVE APPROACHES TO LICENSING, HEALTH TECHNOLOGY ASSESSMENT, AND INTRODUCTION OF DRUGS AND DEVICES
Bibliographic record
Abstract
BACKGROUND: Adaptive approaches to the introduction of drugs and medical devices involve the use of an evolving evidence base rather than conventional single-point-in-time evaluations as a proposed means to promote patient access to innovation, reduce clinical uncertainty, ensure effectiveness, and improve the health technology development process. METHODS: This report summarizes a Health Technology Assessment International (HTAi) Policy Forum discussion, drawing on presentations from invited experts, discussions among attendees about real-world case examples, and background paper. RESULTS: For adaptive approaches to be understood, accepted, and implemented, the Forum identified several key issues that must be addressed. These include the need to define the goals of and to set priorities for adaptive approaches; to examine evidence collection approaches; to clarify the roles and responsibilities of stakeholders; to understand the implications of adaptive approaches on current legal and ethical standards; to determine costs of such approaches and how they will be met; and to identify differences in applying adaptive approaches to drugs versus medical devices. The Forum also explored the different implications of adaptive approaches for various stakeholders, including patients, regulators, HTA/coverage bodies, health systems, clinicians, and industry. CONCLUSIONS: A key outcome of the meeting was a clearer understanding of the opportunities and challenges adaptive approaches present. Furthermore, the Forum brought to light the critical importance of recognizing and including a full range of stakeholders as contributors to a shared decision-making model implicit in adaptive pathways in future discussions on, and implementation of, adaptive approaches.
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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 | 0.370 | 0.393 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.072 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.008 | 0.038 |
| Research integrity | 0.036 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 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".