Comparison of Ultrasonology Findings with Clinical Parameters and Plain Radiograph in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) is one of the commonly encountered joint diseases in people with a prevalence ranging up to 7.9% in adult population. There are various clinical and radiologic instruments to measure the disease severity of knee OA. Western Ontario and McMaster Universities Arthritis Index (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS), Short Form (SF)-12, SF-36 are few commonly used clinical instruments. Kellgren-Lawrence classification (KL) is commonly used radiographic tool to assess the severity of osteoarthritis. Nowadays, high frequency musculoskeletal ultrasonology (MSUS) is being used as a reliable tool for assessment of osteoarthritis. Methods: A prospective, cross sectional study was conducted in patients with knee OA attending a tertiary level rheumatology center in Kathmandu, Nepal. Ethical clearance was taken from NHRC Nepal. Baseline demographic profile of the patients along with clinical parameters like WOMAC and VAS for pain and stiffness were recorded in predesigned data sheet. An AP view standing Xray was obtained for involved knee and KL grading done. High frequency MSUS was done for same knee. Synovial hypertrophy, suprapatellar effusion, cartilage thickness (medial, lateral and intercondylar), Doppler activity and osteophytes were noted. Pearson’s coefficient of correlation was calculated to assess correlation between ultrasonology and radiologic parameters. Results: A total of 138 patients with knee OA were enrolled in the study with female predominance of 83% and mean age of 56.87 ± 10.61 years. The mean VAS for pain and WOMAC scores were 4.95 ± 1.78 and 28.36 ± 13.31, respectively. A negative correlation was observed between cartilage thickness (medial) with joint space narrowing (KL grade) and between WOMAC and VAS scores and cartilage thickness. Conclusions: MSUS may be used in clinical practice to diagnose and monitor cases of knee osteoarthritis. It is a potential imaging technique which might help in therapeutic interventions and disease monitoring too.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".