High-Resolution Peripheral Quantitative Computed Tomography for Bone Evaluation in Inflammatory Rheumatic Disease
Bibliographic record
Abstract
High resolution peripheral quantitative computed tomography (HR-pQCT) is a 3-dimensional imaging modality with superior sensitivity for bone changes and abnormalities. Recent advances have led to increased use of HR-pQCT in inflammatory arthritis to report quantitative volumetric measures of bone density, microstructure, local anabolic (e.g., osteophytes, enthesiophytes) and catabolic (e.g., erosions) bone changes and joint space width. These features may be useful for monitoring disease progression, response to therapy, and are responsive to differentiating between those with inflammatory arthritis conditions and healthy controls. We reviewed 69 publications utilizing HR-pQCT imaging of the metacarpophalangeal (MCP) and/or wrist joints to investigate arthritis conditions. Erosions are a marker of early inflammatory arthritis progression, and recent work has focused on improvement and application of techniques to sensitively identify erosions, as well as quantifying erosion volume changes longitudinally using manual, semi-automated and automated methods. As a research tool, HR-pQCT may be used to detect treatment effects through changes in erosion volume in as little as 3 months. Studies with 1-year follow-up have demonstrated progression or repair of erosions depending on the treatment strategy applied. HR-pQCT presents several advantages. Combined with advances in image processing and image registration, individual changes can be monitored with high sensitivity and reliability. Thus, a major strength of HR-pQCT is its applicability in instances where subtle changes are anticipated, such as early erosive progression in the presence of subclinical inflammation. HR-pQCT imaging results could ultimately impact decision making to uptake aggressive treatment strategies and prevent progression of joint damage. There are several potential areas where HR-pQCT evaluation of inflammatory arthritis still requires development. As a highly sensitive imaging technique, one of the major challenges has been motion artifacts; motion compensation algorithms should be implemented for HR-pQCT. New research developments will improve the current disadvantages including, wider availability of scanners, the field of view, as well as the versatility for measuring tissues other than only bone. The challenge remains to disseminate these analysis approaches for broader clinical use and in research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.003 | 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".