Chance of reimbursement for ADD-ON therapies in Poland and in the world - review of the reimbursement recommendations
Bibliographic record
Abstract
INTRODUCTION: Oncology drugs combined with standard therapies (so-called add-on therapies, e.g. bevacizumab, palbociclib) often receive negative recommendations regarding the legitimacy of public financing, issued by government agencies responsible for their assessment, i.e. health technology assessment agencies. The aim of the study was to estimate the scale of the problem related to the reimbursement of add-on therapies used in the treatment of breast and genitourinary cancers in Poland and in the world. MATERIAL AND METHODS: A multimodal approach was used to select add-on therapies. The reimbursement routes were analysed in 8 reference countries (Poland, Canada, England, Wales, France, Scotland, Australia, New Zealand). Based on a systematic search, data for breast and urogenital cancers were included. RESULTS: A total of 68 reimbursement documents for add-on therapies were identified. The analysis showed that in Poland, 20% of innovative schemes including add-on therapies should be reimbursed, while in the world the percentage of positive recommendations reaches 56%. It was observed that globally (including data for Poland) the chance for a favorable reimbursement recommendation for add-on therapies is 53%, with 29% being positive recommendations with limitations. In Poland, the majority of negative recommendations concern genitourinary cancers in comparison to breast cancer (83% vs 75%). CONCLUSIONS: Poland is at the head of the countries in terms of the number of negative reimbursement recommendations. Bearing in mind the world’s need of modifying the criteria for the evaluation of oncological therapies in the context of the possibility of their reimbursement, one should expect a change in the approach to the assessment of the legitimacy of financing innovative add-on therapies in Poland.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".