MétaCan
Menu
Back to cohort
Record W7117883595 · doi:10.1093/jbmr/zjaf187

External validation of a novel HR-pQCT based fracture risk assessment tool (μFRAC) in a male cohort: the osteoporotic fractures in men (MrOS) study

2025· article· en· W7117883595 on OpenAlexaff
Annabel R Bugbird, Andrew J. Burghardt, Lisa Langsetmo, Kristine E. Ensrud, MARY BOUXSEIN, Douglas P Kiel, Steven K Boyd, Danielle E. Whittier

Bibliographic record

VenueJournal of Bone and Mineral Research · 2025
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsAlberta Bone and Joint Health Institute
FundersNational Center for Advancing Translational SciencesNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on AgingNational Institutes of Health
KeywordsGeneralizability theoryOsteoporotic fractureRisk assessmentCohortRisk management toolsCohort studyOsteoporosis

Abstract

fetched live from OpenAlex

Fracture risk estimates can be used clinically to inform treatment decision-making in osteoporosis. Current fracture risk assessment tools have a low sensitivity in predicting fractures in males. This study aims to evaluate and validate the performance of a new fracture prediction tool-the Microarchitecture Fracture Risk Assessment Calculator ($\mu $FRAC)-in a multicentre cohort (MrOS) of older community-dwelling men. The performance of $\mu $FRAC was assessed in a population of 1586 men aged $\geq 77$ years in the United States. All participants underwent HR-pQCT scanning (61 $\mu $m) of the distal radius and distal tibia. Incident fracture information was collected every 4 months from the study visit. The $\mu $FRAC 5-year and 10-year risk of major osteoporotic fracture and any osteoporotic fracture were calculated for all participants. The model calibration was assessed by fitting fine-gray competing risk regression models. The model discrimination was assessed using receiver operator characteristic curves and area under the curve (AUCs). Over the 10-year follow-up period, 129 men experienced an incident major osteoporotic fracture. The $\mu $FRAC models showed good generalizability of the 5-year risk estimates (regression slope 0.8-1.1) to MrOS cohort. The $\mu $FRAC models displayed an improved model performance (AUC = 0.685-0.703) relative to reference models of FRAX (AUC = 0.641) and FN aBMD alone (AUC = 0.636) for the 5-year major osteoporotic fracture (MOF) risk estimates. A sub-analysis on individuals classified as moderate risk by FRAX (10%-20% MOF risk) found that $\mu $FRAC aided in stratifying risk, particularly for the 5-year risk estimates ($\mu $FRAC AUC = 0.691-0.706). The $\mu $FRAC models demonstrated strong performance and generalizability to an external cohort of older men. This validation of $\mu $FRAC suggests its potential use as an alternate assessment tool for osteoporotic fracture risk and may have value in targeting moderate-risk subgroups to aid treatment decisions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.434
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Bone and Mineral ResearchSame topicBone health and osteoporosis researchFrench-language works237,207