Bone changes assessed with High-Resolution peripheral Quantitative Computed Tomography (HR-pQCT) in early inflammatory arthritis: a 12-month cohort study
Bibliographic record
Abstract
ABSTRACT Objectives We sought to determine the sensitivity of high resolution peripheral quantitative computed tomography (HR-pQCT) to detect change and identify erosions in comparison with conventional radiography (CR) in early inflammatory arthritis patients. We also explored which prognostic factors contribute to bone damage assessed by HR-pQCT in the first year of diagnosis. Methods 46 patients with arthritic symptoms less than one year, and a clinical diagnosis of inflammatory arthritis were prospectively imaged at baseline and 12-months. HR-pQCT scans of the 2 nd and 3 rd MCP joints and CR of the hands and feet were performed. Joint space width (JSW), total bone mineral density (Tt.BMD), erosion presence and volume were assessed with HR-pQCT. Scan-rescan precision was assessed to define an individual-level least significant change (LSC) criterion. Regression analyses explored prognostic factors for bone damage progression. Results We observed no significant group-level changes in JSW, Tt.BMD or erosion volume. 20% or fewer joints demonstrated individual-level changes greater than the LSC criterion for mean JSW, Tt.BMD and erosion volume. HR-pQCT detected more erosions than CR in the 2 nd and 3 rd MCP. Increased symptom duration at diagnosis was associated (p < 0.10) with lower JSW minimum and higher JSW standard deviation. Conclusions We have demonstrated stability in erosion, bone density and JSW over 12-months in most patients receiving treatment for inflammatory arthritis using a LSC criterion, although patients with longer symptom duration prior to treatment initiation had demonstrable negative effects on JSW estimates. HR-pQCT captures bone damage and progression undetectable by CR in the imaged joints.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".