Prescreening for Osteoporosis With Quantitative Ultrasound in Postmenopausal White Women
Bibliographic record
Abstract
OBJECTIVES: Calcaneal quantitative ultrasound (QUS) is a readily accessible and radiation-free alternative to dual-energy x-ray absorptiometry (DXA) for assessing bone mineral density (BMD). Results obtained from QUS measurement cannot directly be compared to DXA, since these techniques capture different bone-specific parameters. To identify individuals who are likely to have osteoporosis by DXA, device-specific thresholds have to be defined for QUS. This cross-sectional study evaluated the accuracy of QUS to identify postmenopausal women with osteoporosis, defined as a T score of -2.5 SDs or lower by DXA, and to calculate device-specific cutoff values for the QUS device investigated. METHODS: We assessed BMD at the lumbar spine, bilateral femoral neck, and total hip sites with DXA and QUS parameters of the right and left calcanei in a cohort of 245 postmenopausal treatment-naïve women between 40 and 82 years. Correlation coefficients for BMD and QUS parameters were calculated. Receiver operating characteristic curves were generated, and areas under the curves (AUCs) were evaluated. Cutoff values for QUS were defined. RESULTS: Calcaneal QUS had the ability to identify postmenopausal women with a T score of -2.5 or lower at the right hip (AUC, 0.887) and left femoral neck (AUC, 0.824). Cutoff values for the QUS T scores at the right (-1.455) and left (-1.480) calcanei were defined for screening purposes. CONCLUSIONS: This study provides insights into the comparative performance of QUS with DXA. Considering the diagnostic accuracy of this modality in comparison to DXA, it can be recommended as a prescreening tool to reduce the number of DXA screenings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".