A systematic review of the association between ultrasound-detected features and laboratory inflammatory biomarkers in hand osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: To systematically review observational studies for the relationship between ultrasound (US)-detected features and laboratory inflammatory biomarkers in hand osteoarthritis (OA). METHODS: A systematic literature search was performed in MEDLINE, EMBASE, CINAHL, and Web of Science from their inception to June 2025 to identify relevant observational studies. Study quality was evaluated using the Newcastle-Ottawa Scale (NOS), with two independent reviewers validating the papers. Correlation coefficients and corresponding confidence intervals and P values between US-detected features and biomarkers were extracted and analysed. RESULTS: = 0.3-0.57) were found between serum inflammatory markers (e.g. TNF, MIP-β, PDGF-bb, IP-10) and grey-scale synovitis (GSS) specifically in erosive hand OA. No significant correlations were observed between other US-detected features (e.g. power Doppler (PD) signals, osteophytes (OST), effusion, cartilage thickness) and inflammatory biomarkers, with coefficients generally <0.2. CONCLUSION: These findings highlight a critical gap in research linking US-detected features and serum inflammatory markers in hand OA. While some evidence suggests that US-detected GSS may reflect subclinical inflammation, particularly in erosive hand OA, inconsistent results across studies underscore the need for larger, standardised research to support phenotyping and inform targeted diagnostic and therapeutic strategies.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".