Evidence map›Paper›PMID 38816562›Full record

ArticleArchives of osteoporosis2024

A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture.

Gianpaolo Voltan, Gennaro Di Giovannantonio, Giovanni Carretta, Stefano Vianello, Cristina Contessa, Nicola Veronese, Maria Luisa Brandi

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Article in Archives of osteoporosis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. An enhanced Fracture Risk Evaluation Model (FREM) using national health data on morbidity and medications.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025
    Article
4 · The record

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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

7 authors.

Gianpaolo VoltanCentre for Metabolic Bone Diseases, Health Authority of Venice Province, Noale, Venice, Italy. gianpaolo.voltan@aulss3.veneto.it.
Gennaro Di GiovannantonioHealth Authority of Venice Province, Mirano, Venice, Italy.
Giovanni CarrettaHealth Authority of Venice Province, Mirano, Venice, Italy.
Stefano VianelloHealth Authority of Venice Province, Mirano, Venice, Italy.
Cristina ContessaMedical Management Unit, University Hospital of Padua, Padua, Italy.
Nicola VeroneseGeriatrics Section, Department of Internal Medicine, University of Palermo, Palermo, Italy.
Maria Luisa BrandiVita-Salute San Raffaele University, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An artificial intelligence-based case-finding strategy has been developed to systematically identify individuals with osteoporosis or at varying risk of fragility fracture. This strategy has the potential to close the critical care gap in osteoporosis treatment in primary care, thereby lessening the societal burden imposed by fragility fractures.

backgroundOsteoporotic fractures represent a major cause of morbidity and, in older adults, a precursor of disability, loss of independence, poor quality of life and premature death. Despite the detrimental health impact, osteoporosis remains largely underdiagnosed and undertreated worldwide. Subjects at risk for osteoporosis-related fractures are identified either via organised screening or case finding. In the absence of a population-based screening policy, subjects at high risk of fragility fractures are opportunistically identified when a fracture occurs or because of other clinical risk factors (CRFs) for osteoporotic fracture and areal bone mineral density (aBMD) measured by dual-energy X-ray absorptiometry (DXA). PURPOSE: This paper describes the development of a novel case-finding strategy, named Osteoporosis Diagnostic and Therapeutic Pathway (ODTP), which enables to identify subjects with osteoporosis or at varying risk of fragility fracture. This strategy is based on a specifically designed software tool, named "Bone Fragility Query" (BFQ), which analyses the electronic health record (EHR) databases of General Practitioners (GPs) to systematically identify individuals who should be prescribed DXA-BMD measurement, vertebral fracture assessment (VFA) and anti-osteoporosis medications (AOM).

conclusionsThe ODTP through BFQ tool is a feasible, convenient and time-saving osteoporosis model of care for GPs during routine clinical practice. It enables GPs to shift their focus from what to do (clinical guidelines) to how to do it in the primary health care setting. It also allows a systematic approach to primary and secondary prevention of fragility fractures, thereby overcoming clinical inertia and contributing to closing the gap between evidence and practice for the management of osteoporosis in primary care.

Indexed as

Artificial IntelligenceOsteoporosisOsteoporotic FracturesAbsorptiometry, PhotonAgedBone DensityFemaleHumansMaleRisk AssessmentRisk FactorsBurden of diseaseFracture risk assessmentFragility fractureHigh fracture riskModels of careOsteoporosisPrimary careScreeningTreatment gap

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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.