Correlations Between Imaging and Clinical, Functional and Biological Features in Knee Osteoarthritis
Bibliographic record
Abstract
Background: For knee osteoarthritis (OA) pathogeny, cartilage damage is important, and ultrasonography (US) is helpful in assessing specific joint issues. Objectives: We intend to find correlations between functionality, pain level, serum glucose, cholesterol, triglycer-ides, uric acid, systemic inflammation and US findings for patients with knee OA. Meth-ods: For 50 consecutive subjects with symptomatic bilateral knee OA staged according to the scale Kellgren-Laurence(K-L) noted anamnestic data, Body Mass Index (BMI), func-tionality evaluated by Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and pain’ intensity through Visual Analogue Scale (VAS). Using the US, the cartilage, meniscal and tendon changes, osteophytes, and fluid collections were assessed for 100 knee joints. SPSS 29.0.2.0 was used for statistical analysis. Results: In our group, with an average age of 60.54 years, there was observed a weak direct correlation between WOMAC and K-L grading (r=0.34) and a negative correlation between BMI and the carti-lage size on the external femoral condyle (r=-0.28). Its thickness on this site was directly correlated with lateral osteophyte severity. Smokers' injuries on the intercondylar groove were increased. Conclusion: Lateral femoral condyle cartilage thickness is inversely asso-ciated with BMI, and K-L grading directly correlates with dysfunctionality. The smokers had higher intercondylar cartilage injuries. Keywords: Knee, osteoarthritis, pain, cartilage, ultrasonography.
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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 | 0.000 |
| 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) | 0.002 | 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".