Longitudinal associations between changes in bone mechanical strength and fracture risk estimated by <b>μ</b> FRAC
Bibliographic record
Abstract
Monitoring bone health for osteoporosis is typically based on measuring areal BMD. However, widely used fracture risk prediction tools are primarily driven by clinical risk factors and show limited sensitivity to underlying bone changes over time. This study evaluated the ability of the new Microarchitecture Fracture Risk Assessment Calculator ($\mu $FRAC) to detect longitudinal changes in fracture risk in relation to bone quality. Our study cohort included 601 participants (70.2% female) from a longitudinal population study. HR-pQCT scans of the distal radius and tibia were acquired at 2 visits, 3-10 years apart. The $\mu $FRAC 5-year risk of major osteoporotic fracture was calculated at both time points. Participants were divided into quartiles based on the absolute change in tibia bone strength between study visits to assess the model's sensitivity to changes in bone fragility. Differences between quartiles were assessed using a Mann-Whitney U test and the standardized response mean (SRM). Additionally, changes in fracture risk by decade, were analyzed to investigate age- and sex-specific trends in fracture risk. The average age of participants was 53.8 $\pm $ 15.4 years, with an average follow-up of 6.8 $\pm $ 1.8 years. The greatest absolute annualized changes in $\mu $FRAC risk occurred in individuals with the largest differences in bone strength (SRM = 0.73-0.78), while the least change was observed in individuals with minimal changes (SRM = 0.07-0.21). Age- and sex-specific trends aligned with previously established patterns of bone aging, showing the greatest annualized changes in fracture risk in menopausal females (40-60 years) and older adults (70+ years). We demonstrated $\mu $FRAC is sensitive to changes in fracture risk driven by declines in bone quality in aging adults. These results suggest $\mu $FRAC is well suited for tracking fracture prediction longitudinally and has potential to monitor osteoporosis disease progression and treatment response.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".