Assessment of Knee Osteoarthritis Severity using New Multifactorial Scale (KHIUS) in Northwest Syria
Bibliographic record
Abstract
The present study aimed to identify the risk factors associated with knee osteoarthritis (OA) in Northwest Syria and to evaluate the reliability of our newly proposed Khatib-Khaled Idlib University Scale (KHIUS) in assessing knee OA severity. The study enrolled 101 patients with knee OA, diagnosed through X-ray at the orthopedic clinic. The Kellgren and Lawrence classification was employed to determine the X-ray knee OA grades. The erythrocyte sedimentation rate (ESR) value was obtained as a biomarker after excluding rheumatoid arthritis, other inflammatory diseases, and malignant tumors. The risk factors of knee OA assessed in our study included age, gender, BMI, and physical activity. Each patient completed the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire and KHIUS to evaluate knee OA severity. Correlation coefficients of two scales, i.e., WOMAC and X-ray knee OA grading as well as KHIUS and X-ray knee OA grading, were determined. The mean age of the patients was 52.84 ± 9.74 years (age range 25-80 years). Most patients had low daily activity levels, and the left knee was the most affected. In our study, the correlation coefficient between WOMAC and KHIUS was strong (R: 80.3%, P < 0.01). The correlation coefficient between X-ray KL knee OA grades and WOMAC was moderate (R: 50.9%, P < 0.01), whereas the correlation coefficient between X-ray KL knee OA grades and KHIUS was comparatively stronger (R: 75.7%, P < 0.01). KHIUS can be a reliable scale to assess knee OA severity and to guide the method of treatment by orthopedic surgeons. In addition, KHIUS is more closely related to X-ray KL knee OA grading than other clinical scales.
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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 | 0.001 |
| 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) | 0.001 | 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".