The clinical application of high-resolution peripheral computed tomography (HR-pQCT) in adults: state of the art and future directions
Bibliographic record
Abstract
High-resolution peripheral computed tomography (HR-pQCT) was developed to image bone microarchitecture in vivo at peripheral skeletal sites. Since the introduction of HR-pQCT in 2005, clinical research to gain insight into pathophysiology of skeletal fragility and to improve prediction of fractures has grown. Meanwhile, the second-generation HR-pQCT device has been introduced, allowing novel applications such as hand joint imaging, assessment of subchondral bone and cartilage thickness in the knee, and distal radius fracture healing. This article provides an overview of the current clinical applications and guidance on interpretation of results, as well as future directions. Specifically, we provide an overview of (1) the differences and reference data for HR-pQCT variables by age, sex, and race/ethnicity; (2) fracture risk prediction using HR-pQCT; (3) the ability to monitor response of anti-osteoporosis therapy with HR-pQCT; (4) the use of HR-pQCT in patients with metabolic bone disorders and diseases leading to secondary osteoporosis; and (5) novel applications of HR-pQCT imaging. Finally, we summarize the status of the application of HR-pQCT in clinical practice and discuss future directions. From the clinical perspective, there are both challenges and opportunities for more widespread use of HR-pQCT. Assessment of bone microarchitecture by HR-pQCT improves fracture prediction in mostly normal or osteopenic elderly subjects beyond DXA of the hip, but the added value is marginal. The prospects of HR-pQCT in clinical practice need further study with respect to medication effects, metabolic bone disorders, rare bone diseases, and other applications such as hand joint imaging and fracture healing. The mostly unexplored potential may be the differentiation of patients with only moderately low BMD but severe microstructural deterioration, which would have important implications for the decision on therapeutical interventions.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".