Evidence map›Paper›PMID 40426310›Full record

ArticleEndocrinology and metabolism (Seoul, Korea)2025

Comprehensive Evaluation of Treatment Patterns in Postmenopausal Patients with Osteoporosis without Fractures: Insights from Tertiary Care Institutions and Nationwide OMOP-CDM Data.

Kyoung Jin Kim, Dachung Boo, Jimi Choi, Hyemin Yoon, Chai Young Jung, Seong Hee Ahn, Namki Hong, Beom-Jun Kim, Ji Seon Oh, Seng Chan You

Abstract read
In one paragraph

Article in Endocrinology and metabolism (Seoul, Korea), 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Smartphone-based patient-reported outcome of acute phase reaction after zoledronic acid infusion and prediction algorithm: a multicenter, prospective cohort study.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
    Article
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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

10 authors.

Kyoung Jin KimDivision of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea.
Dachung BooDepartment of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea.
Jimi ChoiDivision of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea.
Hyemin YoonBig Data Research Center, Asan Institute of Life Science, Asan Medical Center, Seoul, Korea.
Chai Young JungBiomedical Research Institute, Inha University Hospital, Incheon, Korea.
Seong Hee AhnDivision of Endocrinology and Metabolism, Department of Internal Medicine, Inha University Hospital, Inha University College of Medicine, Incheon, Korea.
Namki HongInstitute for Innovation in Digital Healthcare, Yonsei University, Seoul, Korea.
Beom-Jun KimDivision of Endocrinology & Metabolism, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Ji Seon OhBig Data Research Center, Asan Institute of Life Science, Asan Medical Center, Seoul, Korea.
Seng Chan YouDepartment of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea.

Funding

Asan Institute for Life Sciences, Asan Medical Center 2022IP0063Korea Health Industry Development Institute RS-2022-KH125397Ministry of Health and Welfare HR21C0198Ministry of Science and ICTNational Research Foundation of Korea RS-2024-00341426NCIRD CDC HHS U01 IP000063
6 · The paper itself

Abstract

backgruoundOsteoporosis is a global health concern. Despite emerging treatment options for this condition, limited data are available on hospital practices in South Korea. This study addresses the need for a hospital network database that reflects changes in routine clinical practice for osteoporosis in a timely manner.

methodsWe analyzed prescription patterns for anti-osteoporosis medications (AOMs) in postmenopausal women aged ≥50 years diagnosed with osteoporosis between 2012 and 2021 using data from Osteoporosis Analysis and Surveillance Initiative using Standardized data (OASIS) (four tertiary hospitals in South Korea) and a nationwide database from the Health Insurance Review and Assessment (HIRA) Service. AOMs were categorized into antiresorptive and anabolic agents, with a focus on secular changes in the use of oral bisphosphonates, denosumab, selective estrogen receptor modulators (SERMs), and anabolic agents.

resultsIn the OASIS cohort, oral bisphosphonates were the most prescribed first-line AOM (49.0%), followed by denosumab (15.7%) and SERMs (18.0%). Denosumab use increased from 2% in 2016 to 40% in 2020, while oral bisphosphonate use declined from 69% in 2012 to 22% in 2021. The use of anabolic agents, including romosozumab and teriparatide, doubled to 6% after 2019. In the HIRA cohort, parenteral bisphosphonates were most common (54.3%), with significant denosumab use (17.3%).

conclusionPronounced shifts in AOM prescription patterns were observed in South Korea, marked by a notable increase in denosumab prescriptions and a decline in bisphosphonate use. These trends highlight the impact of policy changes and clinical guidelines on osteoporosis treatment and may inform future management strategies.

Indexed as

Bone Density Conservation AgentsOsteoporosis, PostmenopausalPractice Patterns, Physicians'AgedAged, 80 and overDatabases, FactualDenosumabDiphosphonatesFemaleHumansMiddle AgedRepublic of KoreaSelective Estrogen Receptor ModulatorsTertiary Care CentersBone Density Conservation AgentsDenosumabDiphosphonatesSelective Estrogen Receptor ModulatorsDrug prescriptionsOsteoporosis, postmenopausalPractice patterns, physicians’

Identifiers

PMID40426310
PMCPMC12602003

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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.