Accuracy of FRAX® in People With Multiple Sclerosis
Bibliographic record
Abstract
) has been reported to underestimate fracture risk in people with MS when BMD is unknown. We tested FRAX performance for people with MS when BMD is known, and determined if MS is a risk factor for fracture independent of FRAX score. Using population-based databases in Manitoba, Canada, we identified people with MS who underwent BMD screening after MS diagnosis (n = 744) and controls matched on age, sex, and first BMD screening date (n = 3721). We calculated FRAX 10-year probabilities at the BMD screening date, and ascertained incident major osteoporotic fractures (MOF). Using Cox proportional hazards modeling we assessed the effect of MS on the hazard of MOF, adjusting for FRAX 10-year probabilities. MS cases had a higher mean FRAX 10-year probability of MOF calculated with BMD (8.32 ± 7.53) than controls (6.98 ± 5.18; p < 0.01). MS increased the risk for MOF after controlling for FRAX 10-year probability without BMD (HR 1.67; 95% confidence interval [CI], 1.29 to 2.16), and after controlling for FRAX individual risk factors (HR 1.45; 95% CI, 1.12 to 1.89). MS remained a risk factor for MOF even when controlling for FRAX 10-year probability of MOF with BMD (HR 1.48; 95% CI, 1.14 to 1.92). The FRAX 10-year probability with and without BMD underestimated the observed 10-year MOF risk in MS cases by 3% to 5%. Calibration improved when secondary osteoporosis was used to calculate FRAX without BMD. Calibration was best when the rheumatoid arthritis input was used to calculate FRAX probability along with BMD. Using secondary osteoporosis or rheumatoid arthritis as proxies for MS improves performance of FRAX and accurately predicts MOF outcomes in those with MS. This provides clinicians with a readily available approach to improve the accuracy of fracture prediction in MS. © 2019 American Society for Bone and Mineral Research.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".