Evidence map›Paper›PMID 41191170›Full record

ArticleArchives of osteoporosis2025

Introducing FREM

Marlene Rietz, Jan C Brønd, Sören Möller, Jens Søndergaard, Bo Abrahamsen, Katrine Hass Rubin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Archives of osteoporosis, 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.

  1. Pooled it
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

6 authors.

Marlene RietzResearch Unit OPEN, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Jan C BrøndResearch Unit OPEN, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Sören MöllerResearch Unit for Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark.
Jens SøndergaardResearch Unit for General Practice, Department of Public Health, University of Southern Denmark, Odense-Esbjerg, Denmark.
Bo AbrahamsenResearch Unit OPEN, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Katrine Hass RubinResearch Unit OPEN, Department of Clinical Research, University of Southern Denmark, Odense, Denmark. Katrine.Rubin@rsyd.dk.

Funding

Region Syddanmark 22/26197
6 · The paper itself

Abstract

This study used explainable AI to improve the Danish FREM model for predicting one-year risk of major osteoporotic fractures in over 2.4 million individuals aged ≥ 45. A DART boosting algorithm improved performance (AUC 0.77), with explainable outputs aiding clinical interpretation and guiding referrals for fracture risk assessment. PURPOSE: This study aimed to use explainable artificial intelligence to improve the Fracture Risk Evaluation Model (FREM), in the prediction of imminent (one-year) risk of major osteoporotic fractures (MOFs).

methodsFREM

resultsFREM

conclusionThe publicly available FREM

Indexed as

Artificial IntelligenceDecision Support TechniquesOsteoporotic FracturesAgedAged, 80 and overAlgorithmsDenmarkFemaleHumansMaleMiddle AgedOsteoporosisRegistriesRisk AssessmentRisk FactorsCase-findingDecision-supportExplainable AIMachine learningMajor osteoporotic fracturesOsteoporosis

Identifiers

PMID41191170
PMCPMC12589358

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.