Evidence map›Paper›PMID 31583541›Full record

ArticleJournal of bone and mineral metabolism2020

Novel algorithm generating strategy to identify high fracture risk population using a hybrid intervention threshold.

Chung-Yuan Hsu, Chih-Hsing Wu, Shan-Fu Yu, Yu-Jih Su, Wen-Chan Chiu, Ying-Chou Chen, Han-Ming Lai, Jia-Feng Chen, Chi-Hua Ko, Jung-Fu Chen and 1 more

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In one paragraph

Article in Journal of bone and mineral metabolism, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 33% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
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

11 authors at 3 institutions in 1 country.

Chung-Yuan HsuDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
Chih-Hsing WuDepartment of Family Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Shan-Fu YuDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
Yu-Jih SuDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
Wen-Chan ChiuDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
Ying-Chou ChenDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
Han-Ming LaiDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
Jia-Feng ChenDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
Chi-Hua KoDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
Jung-Fu ChenDivision of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Tien-Tsai ChengDivision of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan. tiantsai0919@gmail.com.
Chang Gung University · TWKaohsiung Chang Gung Memorial Hospital · TWNational Cheng Kung University Hospital · TW

Funding

Chang Gung Memorial Hospital CMRPG8D0651
6 · The paper itself

Abstract

introductionThe aim of this study was to develop an algorithm to identify high-risk populations of fragility fractures in Taiwan. MATERIALS AND

methodsA total of 16,539 postmenopausal women and men (age ≥ 50 years) were identified from the Taiwan Osteoporosis Survey database. Using the Taiwan FRAX

results3173 (19.2%) and 3129 (18.9%) participants were categorized to groups A and B, respectively. Participants in group B had a significantly lower BMD (p < 0.001), but clinical characteristics, especially the 10-year probability of MOF (p < 0.001) or HF (p < 0.001), were significantly worse in group A. We found the algorithm generated from the hybrid intervention threshold is practical.

conclusionThe strategy of generating an algorithm for fracture prevention by novel hybrid intervention threshold is more efficient as it identifies patients with a higher risk of fragility fracture and could be a template for other country-specific policies.

Indexed as

AlgorithmsAgedBone DensityFemaleFractures, BoneHip FracturesHumansMaleProbabilityRisk FactorsTaiwanFractureIntervention thresholdsOsteoporosis

Identifiers

PMID31583541
OpenAlexW2978314528

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.