Evidence map›Paper›PMID 39915335›Full record

ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2025

Identifying proximal humerus fractures: an algorithmic approach using registers and radiological visit data.

Tomi Nissinen, Reijo Sund, Sanna Suoranta, Heikki Kröger, Sami P Väänänen

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Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Burden of accumulating fragility fractures on health service usage and costs among post-menopausal women.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
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

5 authors.

Tomi NissinenDepartment of Technical Physics, University of Eastern Finland, POB1627, 70211, Kuopio, Finland. tomi.nissinen@uef.fi.ORCID http://orcid.org/0000-0002-6541-2677
Reijo SundKuopio Musculoskeletal Research Unit, Institute of Clinical Medicine, University of Eastern Finland, POB1627, 70211, Kuopio, Finland.ORCID http://orcid.org/0000-0002-6268-8117
Sanna SuorantaDepartment of Clinical Radiology, Kuopio University Hospital, POB1777, 70211, Kuopio, Finland.
Heikki KrögerKuopio Musculoskeletal Research Unit, Institute of Clinical Medicine, University of Eastern Finland, POB1627, 70211, Kuopio, Finland.ORCID http://orcid.org/0000-0003-4245-8186
Sami P VäänänenDepartment of Technical Physics, University of Eastern Finland, POB1627, 70211, Kuopio, Finland.ORCID http://orcid.org/0000-0001-9803-7407

Funding

State Research Funding 5203101Terveyden Tutkimuksen Toimikunta 2021/338647Terveyden Tutkimuksen Toimikunta 2023/356743
6 · The paper itself

Abstract

In this study, we show that combining register and radiological visit data enables more accurate automated identification of proximal humerus fractures compared to traditional register analysis. In a cohort of 11,863 post-menopausal women, our proposed approach improved the coverage of identified fractures from 74 to 81%. PURPOSE: The aim of this study was to investigate how reliably proximal humerus fractures can be identified from different administrative datasets without manual review.

methodUsing the national medical registers, namely the Care Register for Health Care and the Register for Primary Health Care Visits, as well as the regional radiological image archive PACS, we developed algorithms for automated identification of proximal humerus fractures. In addition to these sources, we used data from patient records as well as from the self-reports gathered by the Kuopio Osteoporosis Risk Factor and Prevention Study (OSTPRE) to establish a gold standard of fractures for validating the algorithms. This gold standard included proximal humerus fractures for a cohort of 11,863 post-menopausal women living in the Kuopio region between 2004 and 2022.

resultsWe report the national registers' yearly accuracy in identifying proximal humerus fractures. During the studied 19-year period, the registers' coverage initially improved but then settled at 75%. We show that the image archive provides almost complete coverage of radiographs for the fracture cases, but excluding false positives poses a challenge. In addition, we propose a simple approach that combines register and radiography visit data to improve the accuracy of automated fracture identification. Our algorithm improves the coverage from 74 to 81% and reduces the false discovery rate from 8 to 7% compared to the traditional register analysis.

conclusionThe proposed approach enables a more reliable way of identifying proximal humerus fractures from administrative data. This study contributes to the objective of automatically tracking all types of fragility fractures in large datasets.

Indexed as

AlgorithmsOsteoporotic FracturesShoulder FracturesAgedAged, 80 and overFemaleFinlandHumansMiddle AgedRegistriesCommunication systemsFragility fractureHumerusImage archiveMedical registerPACSPicture archivingRadiographyRadiological information systemRIS

Identifiers

PMID39915335
PMCPMC12064595

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Registered trials

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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.