The value of super microvascular imaging and shear wave elastography in evaluating synovial lesions in knee osteoarthritis
Bibliographic record
Abstract
Objective This study investigates the value of super microvascular imaging (SMI) and shear wave elastography (SWE) in evaluating the relationship between synovial tissues and disease severity in knee osteoarthritis (KOA) patients. Materials and Methods Patients with KOA who visited our hospital between August 2022 and May 2023 were enrolled. WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) scores, X-ray images (Kellgren-Lawrence grading), power Doppler imaging (PDI), SMI, and SWE were evaluated for each patient. Results A total of 54 patients were enrolled. SMI detected more synovial blood flow than PDI (79.62% vs. 62.96%, p < 0.001). SMI upgraded 22.22% of PDI from level 0 to level 1, 31.48% from level 1 to level 2, and 9.26% from level 2 to level 3. There was a positive correlation between WOMAC scores and Kellgren-Lawrence grading (KLG), (r = 0.79, p < 0.001). PDI showed no significant correlation with WOMAC scores (r = 0.26, p = 0.06) or KLG (r = 0.12, p = 0.40). SMI also showed no significant correlation with WOMAC scores (r = 0.26, p = 0.05) or KLG (r = 0.06, p = 0.67). However, A significant correlation was showcased between PDI (r = 0.38, p = 0.04), SMI (r = 0.47, p < 0.001) and the WOMAC pain scores. Additionally, SWE exhibited significant positive correlations with WOMAC scores (r = 0.74, p < 0.001) and KLG (r = 0.84, p < 0.001). Conclusion In patients with KOA, SMI improved the detection of synovial blood flow and is significantly superior to PDI, offering a more sensitive tool for assessing synovial blood flow. SWE of synovial tissue showed a positive correlation with disease severity, providing a novel tool for assessing the severity of the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".