RELATION BETWEEN Q-ANGLE AND CLINICAL, RADIOGRAPHIC AND ULTRASONOGRAPHIC FINDINGS IN FEMALE PATIENTS WITH SYMPTOMATIC PRIMARY KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
Q-Angle is an important biomechanical factor in assessing the knee joint function. Primary knee osteoarthritis (KOA) is more common in females owing to many factors including increased Q-Angle. Studying the complex biomechanics of the knee joint is essential to diagnose and hence properly treating joint pathology conservatively. Aim of the workThe aim of this work was to study the relation between Q-Angle and clinical, radiographic and musculoskeletal ultrasonographic (US) findings in female patients with symptomatic primary KOA.Patients and Methods:This study had included twenty-five female patients with a mean age of 55.7±4.01 years ranged from 47 to 62 years, collected between June 2018 and October 2019, fulfilling the American College of Rheumatology (ACR) criteria for KOA. Patients were clinically assessed with calculation of the Western Ontario and McMaster Universities Arthritis (WOMAC) index as a functional score. They underwent knee musculoskeletal US examination for evaluation of medial, lateral and inter-condylar distal femoral cartilage thickness and grading. Also conventional radiography of knees were scored using the Kellgren-Lawrence (K-L) grading scale. Spearman’s rho was used to assess the association between Q-Angle value and clinical, functional, radiographic and US findings.
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".