Bibliographic record
Abstract
Reimbursement of drugs is a complex process which requires consideration and balancing of interests of various parties: patients, pharmaceutical manufacturers and payers financing medical services. Poland is an example of a country where no medicine is reimbursed without formal procedures. It is not possible for a drug to be reimbursed in a new medical indication, without an assessment of health technologies. Following the example of other developed countries, Poland in 2005 introduced a health technology assessment system to the drug reimbursement process by establishing an advisory body for the Minister of Health - the Agency for Health Technology Assessment, which tasks include in particular, developing recommendations regarding the financing of health technologies. This article aims at presenting approaches to reimbursement of drugs in the following countries: Poland, United Kingdom, France, the Netherlands, Germany and Canada. Reimbursement process is diverse depending of conditions in particular country. Various approaches are present in terms of: reimbursement of drugs used in hospital and available in pharmacies, generic and innovative drugs, time for which reimbursement decision is issued as well as method of accounting for expenditure for drugs and possible replacement of drugs with cheaper substitutes. In each of the presented countries one can note differences and similarities in terms of: institutions engaged in reimbursement process, significance of assessment and recommendations issued by appropriate institutions, treatment assessment criteria, approach to innovative and generic drugs, instruments used in order to minimize payer’s cost and maximize patients’ access to treatment, actions aiming at unifying financing among particular groups of drugs and general complexity of the whole reimbursement process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".