Evaluating New Medicines for Use in Australian Hospitals: Lessons from North America
Bibliographic record
Abstract
ABSTRACT Several centres were visited in North America to identify strategies that could improve the processes used by Australian hospitals when evaluating new medicines for clinical and economic benefits. The centres visited included hospitals, a managed care organisation and the Canadian Agency for Drugs and Technologies in Health. All of the hospitals visited used a formulary to manage medicines and took a similar approach to evaluating new medicines for formulary listing and monitoring decisions. The institutional pharmacy and therapeutics committees were responsible for decision‐making and were guided by detailed drug monographs prepared by pharmacy staff. There was limited use of pharmacoeconomic data in decision‐making. Resources used to guide decision‐making included standard guidelines for formulary submission, comparative effectiveness reviews and a checklist. A more consistent and rigorous approach to assessing medicines and determining cost‐effectiveness could be achieved in Australian hospitals by using a standard format for submissions. Australian drug and therapeutics committees' members need to be educated about the utility of pharmacoeconomic studies in the evaluation of new medicines.
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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 | 0.035 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads 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".