Osteoporosis Screening in Postmenopausal Women 50 to 64 Years Old: Comparison of US Preventive Services Task Force Strategy and Two Traditional Strategies in the Women's Health Initiative
Bibliographic record
Abstract
The US Preventive Services Task Force (USPSTF) recommends osteoporosis screening for women younger than 65 years whose 10-year predicted risk of major osteoporotic fracture is ≥ 9.3%. For identifying screening candidates among women aged 50 to 64 years, it is uncertain how the USPSTF strategy compares with the Osteoporosis Self-Assessment Tool (OST) and the Simple Calculated Osteoporosis Risk Estimate (SCORE). We examined data (1994 to 2012) from 5165 Women's Health Initiative participants aged 50 to 64 years. For the USPSTF (Fracture Risk Assessment Tool [FRAX] major fracture risk ≥ 9.3% calculated without bone mineral density [BMD]), OST (score <2), and SCORE (score >7) strategies, we assessed sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to discriminate between those with and without femoral neck (FN) T-score ≤-2.5. Sensitivity, specificity, and AUC for identifying FN T-score ≤-2.5 were 34.1%, 85.8%, and 0.60 for USPSTF (FRAX); 74.0%, 70.8%, and 0.72 for SCORE; and 79.8%, 66.3%, and 0.73 for OST. The USPSTF strategy identified about one-third of women aged 50 to 64 years with FN T-scores ≤-2.5. Among women aged 50 to 64 years, the USPSTF strategy was modestly better than chance alone and inferior to conventional SCORE and OST strategies in discriminating between women with and without FN T-score ≤-2.5.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".