Bibliographic record
Abstract
More than half of older women who sustain a fragility fracture do not have osteoporosis by World Health Organization (WHO) bone mineral density (BMD) criteria; and, while BMD has been used to assess fracture risk for over 30 years, a range of other skeletal and nonskeletal clinical risk factors (CRFs) for fracture have been recognized. More than 30 assessment tools using CRFs have been developed, some predicting fracture risk and others low BMD alone. Recent systematic reviews have reported that many tools have not been validated against fracture incidence, and that the complexity of tools and the number of CRFs included do not ensure best performance with poor assessment of (internal or comparative) validity. Internationally, FRAX® is the most commonly recommended tool, in addition to QFracture in the UK, The Canadian Association of Radiologists and Osteoporosis Canada (CAROC) tool in Canada and Garvan in Australia. All tools estimate standard 10-year risk of major osteoporotic and 10-year risk of hip fracture: FRAX® is able to estimate fracture risk either with or without BMD, but CAROC and Garvan both require BMD and QFracture does not. The best evidence for the utility of these tools is in case finding but there may be future prospects for the use of 10-year fracture risk as a common currency with reference to the benefits of treatment, whether pharmacological or lifestyle. The use of this metric is important in supporting health economic analyses. However, further calibration studies will be needed to prove that the tools are robust and that their estimates can be used in supporting treatment decisions, independent of BMD.
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 | 0.041 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".