Clinical Classification of Knee OA Severity Using WOMAC and its Association with Fear of Falling and Functional Capacity
Bibliographic record
Abstract
Aim: To determine whether clinically classifying knee osteoarthritis (OA) using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) is as effective as radiological classification. Methodology: A total of 36 subjects with diagnosed knee OA who visited the physiotherapy OPD of ESI Hospital, Basaidarapur, New Delhi, India, were included in the present study. Procedure: Subjects were screened for cognitive ability using Mini-Mental State Exam (MMSE), fear of falling using Modified Falls Efficacy Scale (MFES) and functional capacity using time up and go test (TUG). Results: We found a significant difference in fear of falling and functional capacity between subjects with mild, moderate and severe OA. Analysis of MFES scores among sub-classification of OA subjects revealed a significant difference (P=0.005) between the groups. Subjects in the mild group had the maximum score thus, displaying the maximum confidence followed by subjects in the moderate and severe subcategories. Analysis of TUG scores showed a significant difference between the three subgroups (P=0.008). Subjects with mild OA had the least average timings followed by subjects in the moderate and severe groups. We also found a significant positive correlation between MFES scores and overall WOMAC scores (P=0.002)). Conclusion: Clinically assessing the severity of OA using WOMAC is an effective method for better assessment and treatment of patients in the absence of radiological evidence. Keywords: Osteoarthritis (OA), The Western Ontario and McMaster Universities Arthritis Index (WOMAC), MFES, timed up and go test (TUG), falls Cite this Article Abhijeet Kaur, Prosenjit Patra, Aditi Patra. Clinical Classification of Knee OA Severity Using WOMAC and its Association with Fear of Falling and Functional Capacity. Research & Reviews: Journal of Medical Science and Technology . 2019; 8(3): 32–38p.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".