Framework for describing and classifying decision-making systems using technology assessment to determine the reimbursement of health technologies (fourth hurdle systems)
Bibliographic record
Abstract
OBJECTIVES: Australia, Canada, and many European countries now use various forms of health technology assessment (HTA) in decision making regarding the reimbursement of drugs and other health technologies. To achieve a better understanding of the potential for use of HTA in this context, an analytical framework was developed to describe and classify existing fourth hurdle systems. METHODS: Based on a review of published literature, and official documentation, the key aspects of a fourth hurdle system were identified at two levels: policy implementation and individual technology decision. Characteristics of the systems were grouped under four main headings: constitution and governance, objectives, use of evidence and decision processes, and accountability. The comprehensiveness and relevance of this framework was assessed by an independent group of experts in HTA. A pilot study was undertaken, using only published sources, to test the feasibility of obtaining the information needed to complete the framework. RESULTS: The framework was found to be sufficiently broad to encompass all the issues of interest regarding the systems, but the proportion of information available from published sources was variable between sections of the framework and between countries, with average availability of 45 percent. CONCLUSIONS: The analytical framework will help researchers and policy makers in individual countries to understand their own systems and will allow some preliminary sharing of experience between countries. More experience of its application is needed to judge whether it will provide the basis for more formal comparison of systems and whether it will determine their appropriateness for particular decision contexts.
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How this classification was reachedexpand
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 | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".