The impact of health technology assessments: an international comparison
Bibliographic record
Abstract
With the rising costs of health care due to an ageing population and a growing number of new and expensive technologies, an increas-ing number of countries have implemented health technology assessments (HTAs) as a means of informing the decision process based on clinical and economic evidence. In an environment where resources are scarce, HTA agencies ’ objective is to ensure access to safe and effective medicines, while managing health care expenditure in an efficient way by reimbursing clinically cost-effective treat-ments. In this discourse, pharmaceutical products are the main – but by no means the only – subjects of such appraisals. Different studies show that the impact of HTAs varies greatly across countries, even though they are assessing the same drug for the same indication.1,2,3 These differences occur because of a number of considerations, such as the national priorities of the moment, the responsibilities and membership of HTA bodies, the differences in processes and time-frames, the implementation or not of the HTA recommendations, or even the ability to engage in price negotiation.4,5 In this issue of Euro Observer we undertake an analysis of health technology appraisals conducted across six agencies with a view to better understanding the similarities and dif-ferences in the appraisal process and the rec-ommendations that follow. The agencies selected are the Common Drug Review (CDR) in Canada, the Pharmaceutical Benefits Advisory Committee (PBAC) in
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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 | 0.134 | 0.336 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.028 | 0.036 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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; 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".