Reimbursement decision-making system in Poland systematically compared to other countries
Bibliographic record
Abstract
Introduction: Our objective was to analyze and compare systematically and structurally reimbursement systems in Poland and other countries. Methods: The systems were selected based on recommendations issued by the Polish Agency for Health Technology Assessment and Tariffication (AHTAPol), which explicitly referred to other countries and agencies). Consequently, apart from Poland, the countries included in the analysis were England, Scotland, Wales, Ireland, France, Netherlands, Germany, Norway, Sweden, Canada, Australia and New Zealand. Relevant information and data were collected through a systematic search of PubMed (Medline), Embase and The Cochrane Library as well as competent authority websites and grey literature sources. Results and discussion: In most of the countries, the submission of a reimbursement application is initiated by a pharmaceutical company, and only a few countries allow it before a product is approved for marketing. All of the agencies analyzed are independent and some have regulatory function of reimbursement decision making body. A key criterion differentiating the various agencies in terms of HTA is the cost-effectiveness threshold. Most of the countries have specific mechanisms to improve access to expensive specialty drugs, including cancer drugs and those used for rare diseases. Reimbursement systems often lack consistency in appreciating the same stages, leading to heterogeneous decision-making processes. The analysis of recommendations issued in different countries for the same medicinal product will allow a better understanding of the relations between the reimbursement system, HTA assessment, stakeholders involvement and decision on reimbursement of innovative drugs.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".