Competitive tenders on analogue hospital pharmaceuticals in Denmark 2017–2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Competitive tenders on pharmaceuticals are one of the most effective cost-containment instruments in healthcare systems. Its effectiveness has been demonstrated, among other things, in markets for generic medicine and biosimilars. In Denmark, an internationally unique model for competitive tenders on analogue substitutable pharmaceuticals has been developed and implemented for all public hospitals. METHODS: We obtained data on all analogue competitive tenders carried out by the Danish Medicines Council from its foundation on January 1, 2017, to October 9, 2020. We calculated univariate descriptive statistics, pairwise correlations and made a multiple regression analysis on tender savings. RESULTS: Average annual saving on hospital pharmaceutical purchase prices was 44.1% ranging from 0.4% to 92.8% between therapeutic areas and areas of indication. There was a significant positive correlation between tender savings and the number of competitors participating in the tender, and a significant negative correlation between tender savings and the number of days since market authorization. CONCLUSIONS: This study finds analogue tenders to be similar in effect and mechanism to competitive tenders in markets for generic medicine and biosimilars. It supports the increasing number of empirical findings that competitive tendering has a high potential to generate substantial savings on healthcare budgets.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it