Performance of FRAX and FRAX-Based Treatment Thresholds in Women Aged 40 Years and Older: The Manitoba BMD Registry
Bibliographic record
Abstract
We examined among women aged ≥40 years the performance of the Fracture Risk Assessment Tool (FRAX) and FRAX-based osteoporosis treatment thresholds under the US National Osteoporosis Foundation (NOF) and UK National Osteoporosis Guideline Group (NOGG) guidelines. We used registry data for all women aged ≥40 years in Manitoba, Canada, with baseline bone mineral density (BMD) testing (n = 54,459). Incident major osteoporotic fracture (MOF), hip fracture, and clinical fracture were assessed from population-based health services data (mean follow-up 10.5 years). Age-stratified hazard ratios (HR) were estimated from Cox regression models. We assessed the sensitivity, specificity, positive predictive value (PPV), number needed to screen (NNS), and number needed to treat (NNT) to prevent a fracture (assuming 20% relative risk reduction on treatment) for osteoporosis treatment thresholds under the NOF and NOGG guidelines. Femoral neck T-score and FRAX (with and without BMD) predicted all fracture outcomes at all ages. There was good calibration in FRAX-predicted versus observed 10-year MOF and hip fracture probability. Overall sensitivity (PPV) for incident MOF was 25.7% (24.0%) for femoral neck T-score ≤ -2.5; 20.3% (26.3%) for FRAX (with BMD)-predicted 10-year MOF risk ≥20% (NOF threshold); 27.3% (22.0%) for FRAX-predicted 10-year MOF risk ≥ age-dependent cut-off (NOGG threshold), 59.4% (19.0%) for the NOF treatment algorithm; and 28.5% (18.4%) for the NOGG treatment algorithm. Sensitivity for identifying incident MOF varied by age, ranging from 0.0% to 26.3% in women 40 to 49 years old and from 49.0% to 93.3% in women aged 80+ years. The gradient of risk for fracture prediction from femoral neck T-score and FRAX (with and without BMD) as continuous measures was strong across the age spectrum. The sensitivity and PPV of the strategies based on dichotomous cut-offs are low, especially among women aged 40 to 49 years (who have lowest incidence rates). Threshold-based approaches should be reassessed, particularly in younger women. © 2019 American Society for Bone and Mineral Research.
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 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".