Relationship Between Q-Angle and Articular Cartilage in Female Patients With Symptomatic Knee Osteoarthritis: Ultrasonographic and Radiologic Evaluation
Bibliographic record
Abstract
OBJECTIVES: This study aims to examine the association between Q-angle and clinical, radiological, and ultrasonographic findings in patients with knee osteoarthritis (OA). PATIENTS AND METHODS: Sixty-eight female patients (mean age 59.8±6.8 years; range 39 to 78 years) diagnosed with knee OA were included in this study and classified into two groups according to Q-angle of symptomatic knees: low Q-angle group (LQ) (n=40) and high Q-angle group (HQ) (n=28) (LQ-angle <15° and HQ-angle ≥15°, respectively). Patients were clinically assessed for pain and functional status by using a visual analog scale and the Western Ontario and McMaster Universities Arthritis Index. X-rays of knees were scored using the Kellgren-Lawrence OA grading system. Symptomatic knees were also evaluated using ultrasonography for distal femoral cartilage thickness/grading. RESULTS: No significant difference was observed in clinical and imaging findings between the groups (p>0.05). HQ-angle measurements were positively correlated with cartilage grading by ultrasonography (r=0.435, p=0.033) and Kellgren-Lawrence grading system (r=0.435, p=0.021), and negatively correlated with cartilage thickness measurements of the medial femoral condyle (r=-0.399, p=0.036). CONCLUSION: We found that HQ-angle was associated with cartilage thickness measurements of the medial femoral condyle and cartilage grading by ultrasonography and the Kellgren-Lawrence grading system in patients with knee OA.
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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 | 0.003 |
| 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 | 0.000 | 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".