Three-dimensional ultrasound reliability of synovial blood flow assessment in thumb osteoarthritis patients
Bibliographic record
Abstract
The basal thumb joint is a prevalent site of osteoarthritis (OA) affecting 15% of people over the age of 30. Inflammation is recognized as a key factor in the disease and its progression. Vascular changes and blood vessel growth are associated with inflammation and contribute to OA progression. Imaging modalities, including ultrasound (US), have been used to visualize and monitor these changes, while also furthering our understanding of the role of inflammation and angiogenesis in OA. US can detect and visualize blood flow with Doppler technologies. These US methods are used in musculoskeletal imaging to evaluate joint inflammation but are limited to two-dimensional assessment. Comprehensive joint imaging is essential to improving our understanding of vascular changes and the role of inflammation in OA. Three-dimensional (3D) US is emerging for musculoskeletal applications, and we have developed a 3D US device for hand and wrist imaging with Doppler capabilities. This paper aims to investigate the test-retest reliability of the 3D Doppler US measures of synovial blood flow in patients with thumb OA. Fifteen patients were imaged two times during an imaging session using 3D Doppler US. Volumetric synovial blood flow measures were determined for the segmented region of synovial inflammation and corresponding Doppler signals. US measures of 3D Doppler signal demonstrated excellent test-retest reliability. This work furthers the development of 3D US imaging tools and measures for musculoskeletal imaging to allow for comprehensive assessments of US imaging features.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".