[Correlation study of superb microvascular imaging on knee osteoarthritis].
Bibliographic record
Abstract
OBJECTIVE: To investigate the correlation between superb microvascular imaging (SMI) blood flow grading, ultrasound semi-quantitative scores, and clinical symptom severity in patients with primary knee osteoarthritis (KOA). METHODS: A total of 94 knees from 47 patients with primary KOA were evaluated. Ultrasound semi-quantitative scoring and synovial SMI grading were performed for each knee joint. Clinical assessments included the Western Ontario and McMaster Universities osteoarthritis index (WOMAC), visual analog scale (VAS) for pain, and serum biomarkers, such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Correlations between clinical scores and ultrasound parameters were analyzed. RESULTS: contained zero). CONCLUSION: The SMI technique demonstrates significantly higher sensitivity than PD in detecting intensity grade of synovial microvascular flow. While SMI compensates for the limitations of PD in identifying low-grade inflammation, its high sensitivity to low- velocity blood flow did not correlate with symptom severity or WOMAC pain scores. These findings suggest that SMI serves as a valuable tool for visualizing microvascular activity in subclinical synovitis, but its role as a direct indicator of clinical symptom severity in KOA remains limited. Further studies with larger sample sizes are warranted to validate its clinical utility.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".