Comparative Review of Added Health Benefits of the Drugs Listed through Economic Evaluation Exemption Procedure in Korea: Cases of France, Germany, and Canada
Bibliographic record
Abstract
This study assessed the additional health benefits of the drugs listed through the Economic Evaluation Exemption Procedure (EEEP) in Korea. We conducted a comparative review of 32 EEEP drugs listed between May 2015 and July 2022, comparing how they were assessed in France, Germany, and Canada. To collect the data, we reviewed the evaluations conducted by the relevant agency in each country and identified if the additional benefit exists and how significant it is. Additionally, the size of the benefit gains assessed by each agency was categorized as “High” or “Low,” allowing us to evaluate the consistency among these countries. In France, only 38% of the 34 drugs compared demonstrated moderate or higher levels of additional benefit. Germany acknowledged substantial benefit improvement in 27% of the 30 drugs assessed, while 73% showed minor, unquantifiable, or no additional benefits. In Canada, 5 out of 22 cases have been identified as providing significant additional benefit. The level of inter-country consistency in the assessment results from these three countries was somewhat limited. Based on the evaluation results in France, Germany, and Canada, the additional benefits of EEEP drugs over existing treatments were not substantial in many cases. Even though the EEEP was introduced to improve accessibility to high-cost drugs for medical conditions with unmet needs, it is necessary to reconsider whether to allow exceptions for drugs with low therapeutic value.
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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 | 0.050 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".