Western Ontario and McMaster Universities Arthritis Index (WOMAC) Optimal Value in Diagnosing Overlapping Fibromyalgia: A Multivariate Study on Knee Osteoarthritis Short running head: WOMAC Value in Diagnosing Overlapping Fibromyalgia
Bibliographic record
Abstract
Abstract Background: In the current study we aim to assess the prevalence of overlapping FM in a population of knee OA patients and to evaluate the diagnostic performance of WOMAC for FM in OA patients. Methods: We recruited a consecutive sample of 100 knee OA patients. They were assessed for pain, stiffness and function using WOMAC and overlapping FM using ACR 2010 criteria. To find independent predictors for fibromyalgia diagnosis, univariate and multivariate logistic regression analyses were utilized. ROC curves and Youden's J index were used to identify the best cutoff values for predictor parameters. Results: 41 in 100 OA patients also had fibromyalgia based on ACR criteria. Age, BMI and WOMAC score were significantly higher in patients with overlapping FM than patients without FM. Univariate analysis identified that the age, BMI and WOMAC score (Ps = 0.029, 0.041, and < 0.001, respectively) are significantly associated with overlapping FM diagnosis. In multivariate analysis, WOMAC score (OR: 0.93 (95% CI 0.90–0.97), P < 0.001) was identified as independent predictors of overlapping FM. Using ROC, the AUC of WOMAC score was 0.715 (95%CI: 0.614–0.817) and the optimum cutoff point for WOMAC for FM was 43.5. Conclusions: It is concluded from this study that WOMAC scores > 43.5 are useful for suggesting FM as a secondary diagnosis in knee OA patients. Future studies are necessary to establish the results of the current study in a more general context, given the limited available evidence.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".