Bibliographic record
Abstract
Nonprescription drugs have become increasingly important in Korean healthcare. By leveraging lower-cost drugs and reducing expenditure associated with fewer physician visits, the nonprescription segment can deliver tremendous value to individual consumers and the Korean healthcare system. Many countries have provided simpler and more rapid routes to market entry for qualifying nonprescription drug products, using the established data on drug safety and efficacy, as well as public and professional opinion. In US, the FDA waived the pre-approval process for over-the-counter (OTC) drugs marketed through the OTC Monograph Process. In Australia and Canada, different OTC product application levels are defined, with a reduced level of assessment required when the risks to consumers are considered low. Japan established a new OTC evaluation system in 2014 to facilitate the Rx-to- OTC switch process. The legislative framework for medicinal products in the European Union allows for drugs to be approved with reference to appropriate bibliographic data for old active substances with well-established uses. Through a comparison of the regulatory framework and the requirements for nonprescription approval process in different countries, several ways to improve regulatory practice for the evaluation of nonprescription drugs in Korea have been suggested.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".