Comparison of Fracture Risk Prediction by the US Preventive Services Task Force Strategy and Two Alternative Strategies in Women 50–64 Years Old in the Women's Health Initiative
Bibliographic record
Abstract
CONTEXT: The United States Preventive Services Task Force (USPSTF) recommends osteoporosis screening for women younger than 65 years whose 10-year predicted risk of major osteoporotic fracture (MOF) is at least 9.3% using the Fracture Risk Assessment Tool. In postmenopausal women age 50-64 years old, it is uncertain how the USPSTF screening strategy compares with the Osteoporosis Self-Assessment Tool and the Simple Calculated Osteoporosis Risk Estimate (SCORE) in discriminating women who will and will not experience MOF. OBJECTIVE: This study aimed to assess the sensitivity, specificity, and area under the receiver operating characteristic curve of the three strategies for discrimination of incident MOF over 10 years of follow-up among postmenopausal women age 50-64 years. SETTING AND DESIGN: This was a prospective study conducted between 1993-2008 at 40 US Centers. PARTICIPANTS: We analyzed data from participants of the Women's Health Initiative Observational Study and Clinical Trials, age 50-64 years, not taking osteoporosis medication (n = 62 492). MAIN OUTCOME MEASURES: The main outcome was 10-year (observed) incidence of MOF. RESULTS: For identifying women with incident MOF, sensitivity of the strategies ranged from 25.8-39.8%, specificity ranged from 60.7-65.8%, and AUC values ranged from 0.52-0.56. The sensitivity of the USPSTF strategy for identifying incident MOF ranged from 4.7% (3.3-6.0) among women age 50-54 years to 37.3% (35.4-39.1) for women age 60-64 years. Adjusting the thresholds to improve sensitivity resulted in decreased specificity. CONCLUSIONS: Our findings do not support use of the USPSTF strategy, Osteoporosis Self-Assessment Tool, or SCORE to identify younger postmenopausal women who are at higher risk of fracture. Our findings suggest that fracture prediction in younger postmenopausal women requires assessment of risk factors not included in currently available strategies.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".