Effect of Race/Ethnicity on United States FRAX Calculations and Treatment Qualification: A Registry-Based Study
Bibliographic record
Abstract
Since 2008. the United States has had four race/ethnic fracture risk assessment tool (FRAX) calculators: White ("Caucasian"), Black, Asian, and Hispanic. The American Society for Bone and Mineral Research Task Force on Clinical Algorithms for Fracture Risk has been examining the implications of retaining race/ethnicity in the US FRAX calculators. To inform the Task Force, we computed FRAX scores according to each US calculator in 114,942 White, 485 Black, and 2816 Asian women (self-reported race/ethnicity) aged 50 years and older. We estimated treatment qualification based upon FRAX thresholds (3% for hip fracture, 20% for major osteoporotic fracture [MOF]). Finally, we examined measures for a hypothetical population-based FRAX calculator derived as the weighted mean for the US population based upon US Census Bureau statistics. With identical inputs, the highest FRAX measurements were found with the White FRAX calculator, lowest measurements with the Black calculator, and intermediate measurements for the Asian and Hispanic calculators. The percentage of women with FRAX scores exceeding the hip fracture treatment threshold was 32.0% for White, 1.9% for Black, and 19.7% for Asian women; the MOF treatment threshold was exceeded for 14.9% of White, 0.0% of Black, and 3.5% of Asian women. Disparities in treatment qualification were reduced after considering additional criteria (fracture history and dual-energy X-ray absorptiometry [DXA] T-score -2.5 or lower). When fracture risk was recalculated for non-White women using the White FRAX calculator, mean values for Asian women slightly exceeded those for White women but for Black women remained substantially below those for White women. When using a single population-based FRAX calculator, the mean probability of fracture and treatment qualification increased for non-White women across the age range. In summary, use of a single population-based FRAX calculator, rather than existing US race/ethnic FRAX calculators, will reduce differences in treatment qualification and may ultimately enhance equity and access to osteoporosis treatment. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".