Independent clinical validation of a Canadian FRAX tool: fracture prediction and model
Bibliographic record
Abstract
A FRAX model for Canada was constructed for prediction of osteoporotic and hip fracture risk using national hip fracture data with and without the use of femoral neck bone mineral density (BMD). Performance of this system was assessed independently in a large clinical cohort of 36,730 women and 2873 men from the Manitoba Bone Density Program database that tracks all clinical dual-energy X-ray absorptiometry (DXA) test results for the Province of Manitoba, Canada. Linkage with other provincial health databases allowed for the direct comparison of fracture risk estimates from the Canadian FRAXmodel with observed fracture rates to 10 years (549 individuals with incident hip fractures and 2543 with incident osteoporotic fractures). The 10-year Kaplan-Meier estimate for hip fractures in women was 2.7 % [95 % confidence interval (CI) 2.1–3.4%] with a predicted value of 2.8 % for FRAX with BMD, and in men the observed risk was 3.5% (95 % CI 0.8–6.2%) with predicted value of 2.9%. The 10-year estimate of osteoporotic fracture risk for all women was 12.0 % (95 % CI 10.8– 13.4%) with a predicted value of 11.1 % for FRAX with BMD, and in men, the observed risk was 10.7 % (95 % CI 6.6–14.9%) with a predicted value of 8.4%. Discrepancies were observed within some subgroups but generally were small. Fracture discrimination based on receiver operating characteristic curve analysis was comparable with publishedmeta-analyses with area under the curve for osteoporotic fracture prediction of 0.694 (95 % CI 0.684–0.705) for FRAX with BMD and for hip fractures 0.830 (95 % CI 0.815–0.846), both of which were better than FRAX without BMD or BMD alone. Individual risk factors considered by FRAXmade significant independent contributions to fracture
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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".