FRAX and fracture prediction without bone mineral density
Bibliographic record
Abstract
The major application of FRAX in osteoporosis is to direct pharmacological interventions to those at high risk of fracture. Whereas the efficacy of osteoporosis treatment, with the possible exception of alendronate, is largely independent of baseline bone mineral density (BMD), it remains a widely held perception that osteoporosis therapies are only effective in the presence of low BMD. Thus, the use of FRAX in the absence of BMD to identify individuals requiring therapy remains the subject of some debate and is the focus of this review. The clinical risk factors used in FRAX have high evidence-based validity to identify a risk responsive to intervention. The selection of high-risk individuals with FRAX, without knowledge of BMD, preferentially selects for low BMD and thus identifies a risk that is responsive to pharmacological intervention. The prediction of fractures with the use of clinical risk factors alone in FRAX is comparable to the use of BMD alone to predict fractures and is suitable, therefore, in the many countries where facilities for BMD testing are sparse. In countries where access to BMD is greater, FRAX can be used without BMD in the majority of cases and BMD tests reserved for those close to a probability-based intervention threshold. Thus concerns surrounding the use of FRAX in clinical practice without information on BMD are largely misplaced.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".