Article in Biomedicines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
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
9 authors.
Ho Suk KangDivision of Gastroenterology, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea.
Joo-Hee KimDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea.
Woo Jin BangDepartment of Urology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea.
Dae Myoung YooHallym Data Science Laboratory, Hallym University College of Medicine, Anyang 14068, Republic of Korea.ORCID 0000-0003-2511-5536
Kyeong Min HanHallym Data Science Laboratory, Hallym University College of Medicine, Anyang 14068, Republic of Korea.
Nan Young KimHallym Institute of Translational Genomics and Bioinformatics, Hallym University Medical Center, Anyang 14068, Republic of Korea.ORCID 0000-0001-9245-8657
Hyo Geun ChoiSuseo Seoul E.N.T. Clinic, 10, Bamgogae-ro 1-gil, Gangnam-gu, Seoul 06349, Republic of Korea.
Ha Young ParkDepartment of Pathology, Busan Paik Hospital, Inje University College of Medicine, Busan 47392, Republic of Korea.
Mi Jung KwonDepartment of Pathology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea.ORCID 0000-0002-2441-0448
Funding
the National Research Foundation of Korea, funded by the Korean Ministry of Science and ICT RS-2025-00556614
6 · The paper itself
Abstract
PubMed holds no abstract for this paper.
Indexed as
chronic kidney diseaselongitudinal follow-up studynationwide health insurance research databaseosteoporosispropensity score overlap weightingrisk assessment
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.
Subgroup-Specific Osteoporosis Risk in Chronic Kidney Disease: Insights from a Nationwide Korean Cohort. · full record | OpenQuestion