External validation of a novel HR-pQCT based fracture risk assessment tool (μFRAC) in a male cohort: the osteoporotic fractures in men (MrOS) study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".