Factors influencing abortion duration, bleeding volume, pain scores, and anxiety levels during medical abortion: a cross-sectional study

This research has been filed with the National Research Filing Center system under file number MR-44-24-032502.

Authors

Institutions

Publication Details

Journal
BMC Women s Health
Published
2025-04-15
DOI
https://doi.org/10.1186/s12905-025-03672-9
Citations
1
Primary Topic
Reproductive Health and Contraception
Type
article
Field-Weighted Citation Impact
1.88

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors influencing abortion duration, bleeding volume, pain scores, and anxiety levels during medical abortion: a cross-sectional study

Wenbo Wu, Yun Gao, Wei Cheng, Qianqian Jiang et al.
1 citations
BMC Women s Health
Reproductive Health and Contraception
1.88
article

Factors influencing abortion duration, bleeding volume, pain scores, and anxiety levels during medical abortion: a cross-sectional study

Wenbo Wu, Yun Gao, Wei Cheng, Qianqian Jiang, Yuehui Zheng, Yanhong Du, Xiangfei Zhu, Liyan Wang, Waixiang Yuan, Wenjuan Wan
article en
1 citations

Abstract

This research has been filed with the National Research Filing Center system under file number MR-44-24-032502.

BMC Women s HealthVol. 25(1)
Guangzhou University of Chinese Medicine (CN), Guangdong Provincial Hospital of Traditional Chinese Medicine (CN), Shenzhen Pingle Orthopedic Hospital (CN)
Shenzhen University, Science, Technology and Innovation Commission of Shenzhen Municipality
Openalex Percentile: Top 17%
Reproductive Health and Contraception
1.88
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.