Bibliographic record
Abstract
Although awareness of pharmaco-economics has increased greatly, its practical use in decision making is, as we have seen in Chapter 4, at best opaque. Some of the issues will be considered in Chapter 6 in the context of the Australian experience in assessing matters of subsidy or reimbursement. This present chapter focuses on identifying barriers and potential solutions to increase the use of economic evidence in the decision making process. Increasingly the pharmaceutical (and device) industries are using economic evidence as part of their submissions to the authorities for determining the reimbursement price of a pharmaceutical or its inclusion in a drug formulary. In part this has been a selective marketing strategy to promote the value added of a specific intervention, but more recently several countries including Australia, Canada, England, Finland, The Netherlands and Portugal have begun to introduce systems which formally link cost effectiveness to reimbursement decisions for new pharmaceuticals and, in some cases, other clinical technologies. Systems of this kind are known in the pharmacoeconomics literature as fourth hurdles or cost-effectiveness hurdles, because in effect they require pharmaceutical firms to demonstrate cost effectiveness before launch, in addition to quality, safety and efficacy, the first three hurdles ordinarily imposed by licensing authorities. Furthermore health technology assessment agencies have been established in most developed countries to provide further information on the clinical effectiveness, and in many (but not all) instances, on the economic impact of a technology [18]. Table 1 provides an overview of the situations in which economic evaluation should be expected to be helpful to decision makers. Welcome though these developments are, evidence of the actual systematic impact of economic evaluation data on decision making remains limited [8,24]. More recently, the EUROMET study examined the use of economic evaluation in Europe and found that few decision makers made use of economic evidence [13]. A similar lack of evidence was reported in a recent European study of evaluations of health care interventions, although some ad hoc evidence of impact was observed [18]. A number of
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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 | 0.397 | 0.616 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.032 | 0.035 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".