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Title Authors PubMed ID
1 Metaphors in context and in isolation: Familiarity, aptness, concreteness, metaphoricity, and structure norms for 300 two-word expressions Pissani L; de Almeida RG; 41491452
PSYCHOLOGY
2 Preprocessing narrative texts in electronic medical records to identify hospital adverse events: A scoping review Jafarpour H; Wu G; Cheligeer CK; Yan J; Xu Y; Southern DA; Eastwood CA; Zeng Y; Quan H; 41072367
ENCS
3 Automated abdominal aortic calcification and trabecular bone score independently predict incident fracture during routine osteoporosis screening Gebre AK; Sim M; Gilani SZ; Saleem A; Smith C; Hans D; Reid S; Monchka BA; Kimelman D; Jozani MJ; Schousboe JT; Lewis JR; Leslie WD; 41071096
ENCS
4 MedCLIP-SAMv2: Towards universal text-driven medical image segmentation Koleilat T; Asgariandehkordi H; Rivaz H; Xiao Y; 40779830
ENCS
5 Contextual variations in the effects of social withdrawal, peer exclusion, and friendship on growth curves of depressed affect in late childhood Commisso M; Persram RP; Lopez LS; Bukowski WM; 40583455
CONCORDIA
6 Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism Cheligeer C; Southern DA; Yan J; Wu G; Pan J; Lee S; Martin EA; Jafarpour H; Eastwood CA; Zeng Y; Quan H; 40105654
ENCS
7 Leveraging Personal Technologies in the Treatment of Schizophrenia Spectrum Disorders: Scoping Review D' Arcey J; Torous J; Asuncion TR; Tackaberry-Giddens L; Zahid A; Ishak M; Foussias G; Kidd S; 39348196
PSYCHOLOGY
8 Context-induced renewal of passive but not active coping behaviours in the shock-probe defensive burying task Alexa Brown 37095421
PSYCHOLOGY
9 A new circuit underlying the renewal of appetitive Pavlovian responses: Commentary on Brown and Chaudhri (2022) Valyear MD; Britt JP; 36700576
CSBN
10 Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications Luo Z; Amayri M; Fan W; Bouguila N; 36685642
ENCS
11 Learning processes in relapse to alcohol use: lessons from animal models Valyear MD; LeCocq MR; Brown A; Villaruel FR; Segal D; Chaudhri N; 36264342
PSYCHOLOGY
12 Supplementary dataset of context-dependent conditioned responding to an alcohol-predictive cue in female and male rats Segal D; Valyear MD; Chaudhri N; 35330738
PSYCHOLOGY
13 Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures Bourouis S; Pawar Y; Bouguila N; 35009726
ENCS
14 Indeterminate and Enriched Propositions in Context Linger: Evidence From an Eye-Tracking False Memory Paradigm Antal C; de Almeida RG; 34744914
PSYCHOLOGY
15 The role of context on responding to an alcohol-predictive cue in female and male rats Segal D; Valyear MD; Chaudhri N; 34742865
PSYCHOLOGY
16 Depressive Symptoms and Social Context Modulate Oxytocin's Effect on Negative Memory Recall Wong SF; Cardoso C; Orlando MA; Brown CA; Ellenbogen MA; 34100542
PSYCHOLOGY
17 Filtration for improving surface water quality of a eutrophic lake. Palakkeel Veetil D, Arriagada EC, Mulligan CN, Bhat S 33310244
ENCS
18 Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing. Ebadi A; Xi P; Tremblay S; Spencer B; Pall R; Wong A; 33230352
ENCS
19 The contribution of dry indoor built environment on the spread of Coronavirus: Data from various Indian states. V AAR, R V, Haghighat F 32834934
ENCS
20 Comparing ABA, AAB, and ABC Renewal of Appetitive Pavlovian Conditioned Responding in Alcohol- and Sucrose-Trained Male Rats. Khoo SY, Sciascia JM, Brown A, Chaudhri N 32116588
PSYCHOLOGY
21 Context controls the timing of responses to an alcohol-predictive conditioned stimulus. Valyear MD, Chaudhri N 32017964
PSYCHOLOGY
22 Biodiversity Observations Miner: A web application to unlock primary biodiversity data from published literature. Muñoz G, Kissling WD, van Loon EE 30692868
BIOLOGY

 

Title:Automated abdominal aortic calcification and trabecular bone score independently predict incident fracture during routine osteoporosis screening
Authors:Gebre AKSim MGilani SZSaleem ASmith CHans DReid SMonchka BAKimelman DJozani MJSchousboe JTLewis JRLeslie WD
Link:https://pubmed.ncbi.nlm.nih.gov/41071096/
DOI:10.1093/jbmr/zjaf144
Publication:Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Keywords:Dual-energy x-ray absorptiometryartificial intelligencebone mineral densitybone texturefracturevascular calcification
PMID:41071096 Category: Date Added:2025-10-10
Dept Affiliation: ENCS
1 Nutrition & Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia.
2 Medical School, The University of Western Australia, Perth, WA, Australia.
3 Centre for AI & ML, School of Science, Edith Cowan University, Perth, Australia.
4 Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
5 Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland.
6 Department of Computer Science, Concordia University, Montreal, Canada.
7 George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Canada.
8 Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.
9 Department of Statistics, University of Manitoba, Winnipeg, Canada.
10 Park Nicollet Clinic and HealthPartners Institute, HealthPartners, Minneapolis, USA.
11 Division of Health Policy and Management, University of Minnesota, Minneapolis, USA.
12 Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.

Description:

Abdominal aortic calcification (AAC), a marker of subclinical cardiovascular disease, has previously shown to be associated with low bone mineral density (BMD) and fracture. However, it remains unclear whether AAC is associated with trabecular bone score (TBS), a gray-level textural measure, or whether it predicts fracture risk independent of this measure. Here, we examined the cross-sectional association of AAC scored using a validated machine learning algorithm (ML-AAC24) with TBS, and their simultaneous associations with incident fractures in 7,691 individuals (93.4% women) through the Manitoba BMD Registry (mean age 75.3 years). The association between ML-AAC24 and TBS was tested using generalised linear regression. Cox proportional hazards models tested the simultaneous relationships of ML-AAC24 and TBS with incident fractures. At baseline, 41.3% of the study cohort had low (<2), 32.4% had moderate (2 to <6) and 26.3% had high (=6) ML-AAC24. Compared to low ML-AAC24, high ML-AAC24 was associated with a 0.81% lower TBS in the multivariable-adjusted model. Independent of each other and multiple established fracture risk factors, ML-AAC24 and TBS were each associated with an increased risk of incident fractures. Specifically, high ML-AAC24 (HR 1.41 95%CI 1.15-1.73, compared to low ML-AAC24) and lower TBS (HR 1.13 95%CI 1.05-1.22, per SD decrease) were associated with increased relative hazards for any incident fracture. High ML-AAC24 and lower TBS were also associated with incident major osteoporotic fracture (HR 1.48 95%CI 1.18-1.87 and HR 1.15 95%CI 1.06-1.25, respectively) and hip fracture (HR 1.56 95%CI 1.05-2.31 and HR 1.25 95%CI 1.08-1.44, respectively). In conclusion, high ML-AAC24 is associated with lower TBS in older adults attending routine osteoporosis screening. Both measures were associated with incident fractures. The findings of this study highlight high ML-AAC24, seen in more than 1 in 4 of the study cohort, and lower TBS provide complementary prognostic information for fracture risk.





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