Role of global femoral cartilage in assessing severity of primary knee osteoarthritis
Bibliographic record
Abstract
Abstract Background/objective Osteoarthritis is a degenerative joint disease marked by structural changes in the joint. Radiological evaluation can be used to assess structural changes. Pain, inflammation, and stiffness are common clinical symptoms, leading to limitations in daily activities. Ultrasound, unlike traditional radiography, allows for a direct examination of changes in soft tissues. In addition, it is sensitive in detecting osteophytes as well as identifying early OA changes in femoral cartilage associated with clinical manifestations and function. Results A cross- sectional study of 40 patients with primary KOA diagnosed according to the American College of Rheumatology (ACR) criteria. After radiographic evaluation using Kellgren-Lawrence (K-L) scale and US examination assessing global femoral hyaline cartilage (GFC), osteophytes, meniscal extrusion, effusion, and Baker’s cyst of the most symptomatic knee, there was significant correlation between (K-L) grading and (GFC) ultrasonographic grading (p = < 0.001). After assessment of pain and functional disability using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale, there was significant correlation between KL and GFC grading with age (p = < 0.001 for both), disease duration (p = < 0.001 for both) as well as WOMAC total scores (p = < 0.001 for both). GFC grading was the only independent predictor relative to other ultrasonographic variables for WOMAC total score (p = < 0.001). Conclusions US is a valid tool to evaluate knee joint space and is well correlated with radiographic images. KOA severity assessed by KL grading and GFC ultrasonographic grading showed good correlation with age, duration of the disease, pain intensity, and functional disability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".