Accuracy of high-resolution peripheral quantitative computed tomography for measurement of bone quality
Bibliographic record
Abstract
The introduction of three-dimensional high-resolution peripheral in vivo quantitative computed tomography (HR-pQCT) (XtremeCT, Scanco Medical, Switzerland; voxel size 82 microm) provides a new approach to monitor micro-architectural bone changes longitudinally. The accuracy of HR-pQCT for three important determinants of bone quality, including bone mineral density (BMD), architectural measurements and bone mechanics, was determined through a comparison with micro-computed tomography (microCT) and dual energy X-ray absorptiometry (DXA). Forty measurements from 10 cadaver radii with low bone mass were scanned using the three modalities, and image registration was used for 3D data to ensure identical regions were analyzed. The areal BMD of DXA correlated well with volumetric BMD by HR-pQCT despite differences in dimensionality (R(2) = 0.69), and the correlation improved when non-dimensional bone mineral content was assessed (R(2) = 0.80). Morphological parameters measured by HR-pQCT in a standard patient analysis, including bone volume ratio, trabecular number, derived trabecular thickness, derived trabecular separation, and cortical thickness correlated well with muCT measures (R(2) = 0.59-0.96). Additionally, some non-metric parameters such as connectivity density (R(2) = 0.90) performed well. The mechanical stiffness assessed by finite element analysis of HR-pQCT images was generally higher than for microCT data due to resolution differences, and correlated well at the continuum level (R(2) = 0.73). The validation here of HR-pQCT against gold-standards microCT and DXA provides insight into the accuracy of the system, and suggests that in addition to the standard patient protocol, additional indices of bone quality including connectivity density and mechanical stiffness may be appropriate to include as part of a standard patient analysis for clinical monitoring of bone quality.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".