3D ultrasound for diagnosis and tracking of synovitis in first carpometacarpal osteoarthritis patients
Bibliographic record
Abstract
First carpometacarpal osteoarthritis (CMC1) is one of the most prominent forms of hand osteoarthritis (OA) with an estimated prevalence of up to 33%. X-ray radiography is the most common imaging modality used in the diagnosis of CMC1 OA. However, studies have reported significant discrepancies between patient-related outcomes and radiographic evidence of OA, which may be attributed to the lack of x-ray soft tissue contrast. Therefore, in conjunction with rapidly expanding soft tissue modalities such as magnetic resonance imaging (MRI) and ultrasound (US) there has been an increased interest in the role that soft tissue structures, such as joint synovium plays in the progression of OA. US and MRI are excellent for imaging soft tissue structures; however, US is highly operator-dependent and is inherently 2D, while MRI is associated with long waitlist times, high operating costs, and inaccessibility to patients with mobility impairments. Three-dimensional (3D) US technology may overcome these limitations by providing a method for bedside monitoring OA progression and treatment response. This paper validates the use of our developed 3DUS device for comparing synovial volume measurements in a CMC OA patient study. The CMC1 patient was imaged to compare the measurement capabilities of 3DUS to MRI for measuring synovial tissue volumes. Results showed that there was a 29.61% difference between acquired MRI and 3DUS volumetric measurements. Furthermore, the coefficient of variance was 1.4% and 5.7% for 3DUS and MRI, respectively, suggesting that the volumetric measurements from the 3DUS image were more consistent and better suited for clinical imaging and assessment of the CMC1 joint.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".