Two Decades of High-Resolution Peripheral Quantitative Computed Tomography: Present and Future Clinical Perspectives
Bibliographic record
Abstract
Twenty years have passed since the introduction of high-resolution peripheral quantitative computed tomography (HR-pQCT) to assess human bone microarchitecture. During that time, the technique has emerged as an important research tool used by clinicians and scientists to learn about the pathophysiology of bone adaptation in the context of osteoporosis and many other bone-affected conditions. Its rich three-dimensional data is well suited for precise longitudinal monitoring of bone microarchitecture and associated patient-specific estimated bone strength.However, uptake of HR-pQCT as a clinical diagnostic tool has been limited, in part due to challenges such as availability, regulatory approvals, and demonstrated cost effectiveness. New research suggests fracture risk assessment using HR-pQCT is comparable with current standards based on traditional bone densitometry, but its contribution to clinical care is best suited to two areas: (1) leveraging microarchitectural information to assist in treatment decisions for the large subset of patients who lie in the so-called gray zone by current fracture risk assessment, and (2) longitudinal monitoring that establishes highly refined trajectories of bone adaptation and can inform decisions to initiate treatment, monitor treatment effects, and inform cessation.
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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 | 0.005 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".