Gender-based analysis of WOMAC scores in moderate and advanced knee osteoarthritis: evaluating pain, stiffness and functional impairment
Bibliographic record
Abstract
Background: Knee osteoarthritis (KOA) significantly impacts mobility and quality of life, with key issues being pain, stiffness, and functional restrictions. With possible differences in the course of the disease and the intensity of symptoms, gender differences in OA symptoms are still uncertain. Using the Western Ontario and McMaster Universities Arthritis Index (WOMAC), this study examined gender-based variations in the burden of OA symptoms between KL grade 2 and KL grade 3 KOA. Methods: According to the EULAR classification, 108 patients with KOA were included in the study. Male and female patients with knee OA underwent WOMAC scores for pain, stiffness, function, and overall discomfort examined. Statistical significance was evaluated using 95% CI, mean differences, and independent t-tests. A p value of less than 0.05 has been considered significant. Results: Across all WOMAC domains, no statistically significant gender differences were discovered, and all comparisons had p values greater than 0.05. In KL grade II, males scored slightly higher on pain and stiffness, but in Kl grade III, these differences decreased. In both grades, the functional and total WOMAC scores were similar for both genders. Conclusions: The results show that among those with knee OA, there are no appreciable differences in symptom load by gender. This highlights the necessity of tailored treatment plans as opposed to gender-specific approaches. Other factors affecting OA outcomes, such as biomechanics, hormonal effects, and lifestyle factors, should be investigated further.
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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 | 0.002 | 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) | 0.002 | 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".