Fracture risk scores using output from an opportunistic screen of low bone density from conventional X-ray
Bibliographic record
Abstract
Fracture risk is commonly assessed by FRAX, a tool that estimates 10-yr risk for major osteoporotic fracture (MOF) and hip fracture. FRAX scores are often refined by including FN BMD measured by DXA as an input. Rho, a novel AI-powered software, estimates FN BMD T-Scores from conventional X-rays, even when FN is not in the image. Whether a FRAX score using this estimate (FRAX-Rho) can improve a FRAX score without a T-Score input (FRAX-NoT) has not been studied. We conducted a retrospective analysis of Canadian Multicentre Osteoporosis Study participants who had X-rays of the lumbar and/or thoracic spine, FRAX risk factors, and DXA T-Scores acquired at the same time point, and follow-up fracture outcomes over 9 yr. In 1361 participants with lumbar X-rays, FRAX-Rho and FRAX with DXA FN T-Scores (FRAX-DXA) had very good agreement in categorizing participants by MOF risk (Cohen's weighted kappa κ = 0.80 [0.77-0.82]), which tended to be better than that between FRAX-NoT and FRAX-DXA (0.76 [0.73-0.79]). Agreement in categorizing participants by hip fracture risk was significantly greater between FRAX-Rho and FRAX-DXA (0.67 [0.63-0.71]) than FRAX-NoT and FRAX-DXA (0.52 [0.48-0.56]). In predicting true incident MOF, FRAX-Rho and FRAX-DXA did not differ in their discriminative power (c-index) (0.76 and 0.77; p = .36); both were significantly greater than that of FRAX-NoT (0.73; p < .004). The accuracy of FRAX-Rho for predicting MOF (Brier Score) was better than FRAX-NoT (p < .05) but not as good as FRAX-DXA. Similar results were observed in participants with thoracic X-rays. In conclusion, FN T-Scores estimated by Rho from lumbar and thoracic X-rays add value to FRAX-NoT estimates and may be useful for risk assessment when DXA is not available.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".