Prescription, Dispensation, and Generic Medicine Replacement Ratios: Influence on Japanese Medicine Costs
Bibliographic record
Abstract
This study used publicly available data to examine the effect of the separation of dispensing and prescribing medicines between pharmacists in pharmacies and doctors in medical institutions (the separation system) and the generic medicine replacement ratio on the cost of various medicines in Japanese prefectures. For Japanese medical institutions, participation in the separation system is optional. Consequently, the expansion rate of the separation system for each administrative district is highly variable. In our multiple regression analysis, the dependent variables were the costs of daily medicines, specifically, total, internal, external, and injection medicines, as well as medical devices, and the independent variables were the expansion rate of the separation system and generic medicine replacement ratio. The expansion rate of the separation system showed a significant negative partial correlation with the daily costs of total, internal, and injection medicines as well as medical devices. Moreover, the rate of replacing brand name medicines with generic medicines showed a significant negative partial correlation with the daily costs of total and internal medicines. However, external and injection medicines and medical devices did not because only a few or no generic products of these types were sold in the Japanese market. Otherwise, expansion of the separation system was effective in reducing medicine costs, except in the case of external medicines. This suggests that the cost efficiency effect of the separation system does not function all the time.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".