Short-term Precision Error in Dual Energy X-Ray Absorptiometry, Bone Mineral Density and Trabecular Bone Score Measurements; and Effects of Obesity on Precision Error
Bibliographic record
Abstract
Introduction: Bone mineral density (BMD) measured by dual energy x-ray absorptiometry (DXA) is the primary screening tool for diagnosis of osteopenia and osteoporosis. BMD alone does not provide information regarding the structural characteristics of bone and this limitation has been a driver for the development of techniques, including trabecular bone score (TBS) software, to assess bone microarchitecture. Precision error in DXA is important for accurately monitoring changes in BMD and it has been demonstrated that BMD precision error increases with increasing body mass index (BMI). Information on in vivo precision error for TBS is very limited. This study evaluated short-term precision error (STPE) of lumbar spine BMD & TBS measurement, and investigated the effect of obesity on DXA precision error. Method: DXA lumbar spine scans (L1-L4) were performed using GE Lunar Prodigy. STPE was measured in ninety-one women at a single visit by duplicating scans with repositioning in-between. Precision error was calculated as the percentage coefficient of variation. Participants were sub-divided into four groups based on BMI to assess the effect of obesity on STPE. Results: STPE is poorer for TBS than for BMD. STPE is adversely affected for both BMD and TBS measurements by increasing BMI but this effect is mitigated for TBS in the highest BMI category where use of the thick scanning mode improves signal to noise ratio. Conclusion: Results from serial BMD and TBS measurements should take account of differences in precision error in the two techniques and in different BMI categories.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".