Transparency in pricing arrangements for medicines listed on the Australian Pharmaceutical Benefits Scheme
Bibliographic record
Abstract
Australia's system for assessing the cost-effectiveness of drugs for listing under the Pharmaceutical Benefits Scheme (PBS) is recognised internationally. A variety of mechanisms, such as evidence-based rules for determining eligibility for initial or continuing subsidy, price-volume agreements, rebates, and caps on government expenditure are used to contain PBS expenditures. In this paper we assess the extent of use of special pricing arrangements in Australia and how and where they are communicated to health professionals and the community. We searched publicly available documents published by the Pharmaceutical Benefits Advisory Committee (PBAC) and the Pharmaceutical Benefits Pricing Authority (PBPA). We found 73 medicines where special pricing arrangements had been applied and where prices appearing on the Schedule of Pharmaceutical Benefits might differ from those considered to be "cost-effective" by the PBAC. Reporting of these special pricing agreements was inconsistent and generally non-transparent. In some, the lack of transparency may have reflected the desire of manufacturers to disguise the true negotiated price, lest it weaken their negotiation position in other jurisdictions.
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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 | 0.195 | 0.464 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".