Analysis of the Quality of Life Domain in Knee Injury an Osteoarthritis Outcome Score as an Expansion of Western Ontario and McMaster Universities Osteoarthritis Index for Evaluating Knee Osteoarthritis Therapy Outcomes
Bibliographic record
Abstract
Introduction : Knee osteoarthritis is a degenerative joint disease causing pain and functional limitation, with rising prevalence globally and in Indonesia. Outcomes are commonly assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), which evaluates pain, stiffness, and function, but excludes quality of life (QOL). Rehabilitation medicine emphasizes not only symptom relief but also recovery and life quality. The Knee Injury and Osteoarthritis Outcome Score (KOOS) adds a QOL domain for more comprehensive evaluation. Method: A pre-post observational study was conducted on 24 knee osteoarthritis patients receiving ultrasound diathermy and transcutaneous electrical nerve stimulation (USD TENS). KOOS scores for pain, activities of daily living (ADL), and QOL were recorded before and after intervention. Paired t-tests or Wilcoxon tests analyzed score changes, while Pearson or Spearman tests assessed correlations between ?QOL and ?pain, ?ADL, and ?WOMAC. Result: KOOS scores improved significantly after therapy (pre = 58.96; post = 33.29; p < 0.001), including QOL (p = 0.002), pain (pre = 52.50; post = 30.63; p = 0.000), and ADL (pre = 58.13; post = 37.92; p = 0.000). However, QOL changes were not significantly correlated with ?pain (r = 0.399; p = 0.053), ?ADL (? = 0.306; p = 0.146), or ?WOMAC (? = 0.356; p = 0.088). Conclusion: The QOL domain in KOOS reflects a distinct dimension not captured by WOMAC. KOOS thus offers a more holistic and patient-centered evaluation of therapeutic outcomes, highlighting the importance of including QOL in rehabilitation medicine. Keywords: knee osteoarthritis, KOOS, WOMAC, quality of life, USD TENS
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".