Contrast-enhanced Coded Phase-inversion Harmonic Sonography of Knee Synovitis Correlates with Histological Vessel Density: 2 Automated Digital Quantifications
Bibliographic record
Abstract
OBJECTIVE: To use contrast-enhanced coded phase-inversion harmonic B-mode sonography to assess the acoustic enhancement of the synovial area of the knee; and to compare the data with the histological vessel density. METHODS: Eleven patients eligible for a knee arthroscopy were studied. Acoustic quantification was carried out by a digital image analysis program that detects the time-dependent increase [intensity (time) = k x time + C] of gray-level intensity in all the pixels of a specific region of interest (ROI) following intravenous injection of the microbubble contrast agent sulfur hexafluoride. Echo-guided synovial biopsies were carried out in the same ROI. Synovial vessel areas were quantified after Factor VIII immunostaining of synovial biopsies using an automated digital image analysis. RESULTS: Significant (p < 0.05) correlations were observed between histological vessel density and percentage of the synovial area with a k value > 0.01 (r = 0.93) and k(max) values (r = 0.79), as well as between the 2 latter parameters (r = 0.72). The histological vessel density and the 2 acoustic parameters were also significantly correlated with the logarithm of erythrocyte sedimentation rate (r = 0.77, r = 0.87, r = 0.67, respectively) and with log C-reactive protein serum concentration (r = 0.69, r = 0.83, r = 0.62, respectively). CONCLUSION: Contrast-enhanced coded phase-inversion harmonic B-mode sonography coupled with an appropriate data analysis method is a new tool to identify and quantify vessel density in knee synovitis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.001 | 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".