Does osteoporosis therapy invalidate FRAX for fracture prediction?
Bibliographic record
Abstract
Ten-year fracture risk assessment with the fracture risk assessment system (FRAX) is increasingly used to guide treatment decisions. Osteoporosis pharmacotherapy reduces fracture risk, but the effect is greater than can be explained from the increase in bone mineral density (BMD). Whether this invalidates fracture predictions with FRAX is uncertain. A total of 35,764 women (age ≥50 years) and baseline BMD testing (1996–2007) had FRAX probabilities retroactively calculated. A provincial pharmacy database was used to identify osteoporosis medication use. Women were categorized as untreated, current high adherence users [medication possession ratio (MPR) ≥0.80 in the year after BMD testing], current low adherence users (MPR <0.80), and past users. Fractures outcomes to 10 years were established form a population-based health data repository. FRAX and femoral neck BMD alone stratified major osteoporotic and hip fracture risk within untreated and each treated subgroup (all p-values <0.001) with similar area under the receiver operating characteristic curve. In untreated and each treated subgroup, a stepwise gradient in observed 10-year major osteoporotic and hip fracture incidence was found as a function of the predicted probability tertile (all p-values <0.001 for linear trend). Concordance (calibration) plots for major osteoporotic fractures and hip fractures showed good agreement between the predicted and observed 10-year fracture incidence in untreated women and each treated subgroup. Only in the highest risk tertile of women highly adherent to at least 5 years of bisphosphonate use was observed hip fracture risk significantly less than predicted, though major osteoporotic fracture risk was similar to predicted. In summary, this work suggests that the FRAX tool can be used to predict fracture probability in women currently or previously treated for osteoporosis. Although FRAX should not be used to assess the reduction in fracture risk in individuals on treatment, it may still have value for guiding the need for continued treatment or treatment withdrawal
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".