Multicenter precision of cortical and trabecular bone quality measures assessed by high-resolution peripheral quantitative computed tomography
Bibliographic record
Abstract
High-resolution peripheral quantitative computed tomography (HR-pQCT) has recently been introduced as a clinical research tool for in vivo assessment of bone quality. The utility of this technology to address important skeletal health questions requires translation to standardized multicenter data pools. Our goal was to evaluate the feasibility of pooling data in multicenter HR-pQCT imaging trials. Reproducibility imaging experiments were performed using structure and composition-realistic phantoms constructed from cadaveric radii. Single-center precision was determined by repeat scanning over short-term (<72 hours), intermediate-term (3-5 months), and long-term intervals (28 months). Multicenter precision was determined by imaging the phantoms at nine different HR-pQCT centers. Least significant change (LSC) and root mean squared coefficient of variation (RMSCV) for each interval and across centers was calculated for bone density, geometry, microstructure, and biomechanical parameters. Single-center short-term RMSCVs were <1% for all parameters except cortical thickness (Ct.Th) (1.1%), spatial variability in cortical thickness (Ct.Th.SD) (2.6%), standard deviation of trabecular separation (Tb.Sp.SD) (1.8%), and porosity measures (6% to 8%). Intermediate-term RMSCVs were generally not statistically different from short-term values. Long-term variability was significantly greater for all density measures (0.7% to 2.0%; p < 0.05 versus short-term) and several structure measures: cortical thickness (Ct.Th) (3.4%; p < 0.01 versus short-term), cortical porosity (Ct.Po) (15.4%; p < 0.01 versus short-term), and trabecular thickness (Tb.Th) (2.2%; p < 0.01 versus short-term). Multicenter RMSCVs were also significantly higher than short-term values: 2% to 4% for density and micro-finite element analysis (µFE) measures (p < 0.0001), 2.6% to 5.3% for morphometric measures (p < 0.001), whereas Ct.Po was 16.2% (p < 0.001). In the absence of subject motion, multicenter precision errors for HR-pQCT parameters were generally less than 5%. Phantom-based multicenter precision was comparable to previously reported in in vivo single-center precision errors, although this was approximately two to five times worse than ex vivo short-term precision. The data generated from this study will contribute to the future design and validation of standardized procedures that are broadly translatable to multicenter study designs.
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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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.000 | 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".