Construct validity of a clinically correlated knee osteoarthritis ultrasonographic scale: a cross-sectional observational study
Bibliographic record
Abstract
OBJECTIVES: To assess the validity of a novel ultrasonographic scale for knee osteoarthritis (KOA) and its relation with the degree of pain and clinical features. DESIGN: A cross-sectional observational study including 245 patients with knee pain who fulfilled the American College of Rheumatology criteria for KOA. All patients were subjected to clinical assessment Western Ontario and McMaster Universities Index of Osteoarthritis (WOMAC) scale, global visual analogue scale (VAS) and functional assessment through the use of Health Assessment Questionnaire-II (HAQ-II). Ultrasonographic assessment was conducted following ZAGAZIG scale comprising five domains: one for degenerative features (one-third of total score) and the other four for inflammatory features (two-thirds of score). RESULTS: There were positive correlations between ZAGAZIG score and all WOMAC subscales (pain, stiffness and function) (r=0.71, p=0.00; r=0.62, p=0.00; r=0.70, p=0.00, respectively).Moreover, there was a strong positive correlation between ZAGAZIG scale and both VAS and HAQ-II (r=0.82, p=0.00 and r=0.67, p=0.00). At cut-off point of >5, total US score distinguishes between patients with KOA with no or mild pain and those with moderate pain (87.2 sensitivity and 87.2 specificity). At cut-off point of 7, total score of ZAGAZIG scale distinguishes between patients with KOA with moderate pain and those with severe pain (94.5 sensitivity and 42.5 specificity). CONCLUSION: ZAGAZIG scale constitutes a validated tool for KOA assessment. ZAGAZIG scale correlated with WOMAC subscales (pain, stiffness and function), VAS and HAQ.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".