Ultrasonography findings in knee osteoarthritis: a prospective observational cross-sectional study of 100 Patients
Bibliographic record
Abstract
Introduction: Worldwide, knee osteoarthritis (KOA) accounts for 2.2% of total years lived with disability. There is low correlation between joint tissue damage and pain intensity. Periarticular structures may be involved and cannot be identified in X-rays. Objectives: To describe the main ultrasonography (USG) changes in symptomatic patients with primary KOA; to correlate number of USG findings with KOA severity assessed by Kellgren and Lawrence (K&L) radiological scores, with pain intensity measured by a visual analogue scale (VAS) and with functioning scores assessed with Timed up and go test (TUG) and Western Ontario and McMaster Universities (WOMAC) questionnaire. Methods: 100 patients with primary symptomatic KOA were assessed with X-ray and USG. Quantitative and qualitative analyses were evaluated in a systematic manner. Results: The most frequent findings were joint effusion, pes anserinus bursitis, quadriceps tendon enthesopathy, popliteal cyst, iliotibial band tendinitis and patellar tendinitis. Pearson’s correlation analysis demonstrated a significant moderate positive association between VAS scores and number of USG findings (r=0.36; p<0.0001). The number of USG findings was different between K&L grades I and III (p=0.041), I and IV (p<0.001), and II and IV (p=0.001, ANOVA with Bonferroni correction). There was significant association between number of USG findings and TUG (r=0.18; p=0.014) and WOMAC scores for pain (r=0.16; p<0.029) and physical function domains (r=0.16; p<0.028). Conclusion: The most frequent USG finding was joint effusion. Periarticular structures should be explored as potential sources of pain and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".