External validation of a novel HR-pQCT fracture risk assessment tool (<b>μ</b>FRAC) in a female cohort: Sahlgrenska University Hospital Prospective Evaluation of Risk of Bone Fractures (SUPERB) study
Bibliographic record
Abstract
The Microarchitecture Fracture Risk Assessment Calculator ([Formula: see text]FRAC) was developed to predict osteoporotic fracture risk using direct measures of bone microarchitecture. However, its performance has not yet been externally validated in women. This study aims to evaluate the performance of the [Formula: see text]FRAC model in predicting osteoporotic fracture risk in a cohort of older women. The performance of [Formula: see text]FRAC was assessed in a population of 2307 postmenopausal women aged 75-80 yr in Gothenburg, Sweden. All participants underwent HR-pQCT scanning of the distal radius and distal tibia (82 [Formula: see text]m). Incident fracture information was collected from regional X-ray archives up until March 2023. The [Formula: see text]FRAC 5- and 10-yr risk of major osteoporotic fracture (MOF) and any osteoporotic fracture were calculated for all participants. The model discrimination was assessed using receiver operator characteristic (ROCs) curves and area under the curve (AUCs). FRAX and femoral neck areal bone mineral density (FN aBMD) were used as reference models for comparison. During the follow-up period (mean 6.82 [Formula: see text] 2.48 yr), 584 participants (25.3%) sustained an MOF. The AUCs for MOF models for 5- and 10-yr predictions were similar for [Formula: see text]FRAC (AUC = 0.601-0.634) compared with reference models of FRAX (AUC = 0.615-0.618) and FN aBMD (AUC = 0.596). Overall, [Formula: see text]FRAC showed good generalizability (regression slope 0.82-1.00) to the external cohort. Fracture risk prediction by [Formula: see text]FRAC was demonstrated to perform consistently with FRAX and FN aBMD in this external cohort of elderly females, suggesting it is a viable alternative fracture risk prediction tool. A benefit of [Formula: see text]FRAC is that it is based on bone microarchitecture rather than binary clinical risk factors, so it has potential to be sensitive to changes in bone fragility as a function of age-related deterioration or anti-osteoporosis treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".