Evidence map›Paper›PMID 40066008›Full record

ArticleFrontiers in public health2025

Comprehensive evaluation of the development of traditional Chinese medicine industry in Shaanxi province based on PMC index model.

Xiaoying Zhu, Shuzhi Lin, Wei Liu, Qian Liu, Lin Yin, Bianling Feng

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiaoying ZhuDepartment of Pharmacy Administration, School of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Shuzhi LinDepartment of Pharmacy Administration, School of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Wei LiuDepartment of Pharmacy Administration, School of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Qian LiuDepartment of Pharmacy Administration, School of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Lin YinDepartment of Pharmacy Administration, School of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Bianling FengDepartment of Pharmacy Administration, School of Pharmacy, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The high-quality development of the traditional Chinese medicine (TCM) industry is dependent on supportive policies and requires higher levels of coordination and integration. National and local government policies must coordinate the integrated development of the TCM industry under modern governance principles. However, the policy structure and its impact on the development of the TCM industry have not been thoroughly explored from the perspectives of policy design and empirical evidence. Methods: A TCMIDPs evaluation system was established using the PMC-Index model to quantitatively analyze TCMIDPs in China and Shaanxi Province. Additionally, principal component analysis (PCA) was used to comprehensively evaluate 51 enterprises in Shaanxi Province. Results: The policy analysis shows that the average PMC index of the 31 TCM industry development policies is 5.080, indicating that the areas and functions covered by these policies need improvement. The comprehensive evaluation reveals that pharmaceutical enterprises in Shaanxi Province still exhibit unbalanced development. Conclusion: Future TCM industry policies should focus on long-term industrial development, incorporating comprehensive perspectives. Emphasis should be placed on strengthening enterprise R&D, accelerating pharmaceutical innovation, and promoting the holistic development of pharmaceutical enterprises.

Indexed as

Drug IndustryIndustrial DevelopmentMedicine, Chinese TraditionalChinaHumansPrincipal Component Analysiscomprehensive evaluationPMC-index modelprincipal component analysis (PCA)traditional Chinese medicine policiestraditional Chinese medicine (TCM) industry development

Identifiers

PMID40066008
PMCPMC11891234

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.