International comparison of systems to determine entitlements to medical specialist care: performance and organizational issues
Bibliographic record
Abstract
Summary \nObjective:\nCVZ has asked us to provide a comparison of criteria and procedures that different countries use to determine entitlements to medical specialist care. This question was asked within the context of the recent introduction of the DBC (diagnosis treatment combinations) system as an alternative to existing methods of financing of hospital services.\n\nMethods\nThe analysis covered priority systems in nine countries: Australia, Belgium, Canada, France, Germany, the Netherlands, Sweden, Switzerland, and the UK. To meaningfully compare existing criteria and procedures of different countries and analyze the possibilities and limitations of priority setting systems, we used an\nanalytical framework for international comparison recently developed by Hutton and co-workers (Hutton et al., 2006). The framework was created to encompass the many aspects of fourth hurdle systems. It can deal with the legal and political characteristics at the system level and the detailed nuances of varying assessment and decision-making procedures at the decisional level. It analyses priority systems at two\nlevels:\n1. Policy implementation: the establishment of the fourth hurdle system as a policy decision of the government, the policy objectives of the system, its legal status, and its relationships with the remainder of the health system, with other public sector bodies, and with other stakeholders, such as industry and patient groups;\n2. Individual technology decision: the processes by which individual technologies are dealt with by the system, for example, assessment pr
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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 | 0.078 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".