Implementation and outcomes of China’s drug patent linkage system: lessons from international experience
Bibliographic record
Abstract
INTRODUCTION: The drug patent linkage system plays a crucial role in achieving a balance between protecting drug innovation and ensuring the accessibility of generic drugs. Currently, this system has been implemented in many countries. This review aims to provide policy guidance and a reference basis for relevant stakeholders, while also offering valuable insights to other countries considering the implementation of the patent linkage system globally. AREAS COVERED: This review provides a comprehensive summary and comparative analysis of the patent linkage systems in the United States, Canada, South Korea, Singapore, and China, with a focus on China's recently implemented system and its outcomes. The data presented are derived from the official websites of the China NIPA, the IPC, and the NMPA's Drug Patent Information Registration Platform. EXPERT OPINION: This main goal of this system is to balance the interests of both parties through an efficient dispute resolution mechanism, while policy formulation is only the initial stage of the reform process and requires continuous improvement throughout the implementation phase. The author employed descriptive statistical methods to systematically analyze relevant data, providing valuable insights to colleagues in the field both domestically and internationally.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".