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Record W4413414271 · doi:10.1080/13543776.2025.2548586

Implementation and outcomes of China’s drug patent linkage system: lessons from international experience

2025· review· en· W4413414271 on OpenAlexaboutno aff
Yu Chen, Xinjuan Liu, Suju Li, Zhenyu Xu

Bibliographic record

VenueExpert Opinion on Therapeutic Patents · 2025
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessDrugLinkage (software)ChinaPharmacologyMedicineProcess managementPublic relationsPolitical scienceBiologyGeneGenetics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.262
GPT teacher head0.382
Teacher spread0.121 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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