Imaging-based measures of synovitis in knee osteoarthritis: A scoping review and narrative synthesis
Bibliographic record
Abstract
Background: Synovitis has emerged as a tissue target of importance in OA research and is commonly evaluated with medical imaging. Objectives: The purpose of this scoping review was to identify and describe the medical imaging techniques and definitions used by knee OA researchers to assess synovitis, summarize their advantages and disadvantages, and identify opportunities for future work. Eligibility criteria: We included full-text peer-reviewed English publications including adults diagnosed with knee OA. Studies were included if one or more medical imaging modalities were used to assess synovitis in the knee.Studies of inflammatory arthritis, joint replacement, and synovial joints other than the knee were excluded. Animal studies and literature syntheses were also excluded. Sources: MEDLINE, SCOPUS, and Google scholar databases were systematically searched for publications (2000-2023) using the following medical subject headings (MeSH): "osteoarthritis, knee", "magnetic resonance imaging", "ultrasonography", "synovitis". Results: 1154 articles were identified from searching medical databases. After removal of duplicates, abstract screening, and full text reading, 251 articles were included in the final review. MRI is the most common modality employed to assess knee synovitis, followed by US imaging. Varied imaging techniques used in the assessment of joint synovitis may be targeting divergent constructs of synovial remodeling and inflammation, which complicates interpretation of results. Conclusions: There is no consensus on the best method for imaging of knee synovitis in OA. Future work may benefit from the evaluation of synovitis separate from joint effusion, and their associations with histologic findings to discriminate between features of synovial inflammation and remodeling.
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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 | 0.036 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".