COMPARING QUALITY OF LIFE AMONG PEOPLE WITH DIFFERENT PATTERNS AND SEVERITIES OF KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
Objective: To identify what extent different patterns and severities of involvement affect quality of life of people suffering knee osteoarthritis. Methods: This population-based survey involved 288 women and 288 men aged 40 years or older from Songkhla province, southern Thailand. Quality of life was measured using the Medical Outcome Study Short Form Health sutvery (SF-36) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Radiographic investigation included antero-posterior and skyline view of both knees. Osteoarthritis was categorized into 3 patterns; isolated patellofemoral, isolated tibiofemoral and combined with diagnosis based on Kellgren & Lawrence grade 2 or higher. Results: Quality of life as measured by SF-36 and WOMAC showed poorer score in moderate or severe grade than in mild grade of severity. Isolated patellofemoral and combined patterns demonstrated showed poorer scores on both WOMAC and SF-36 than isolated tibiofemoral pattern. Body mass index, income level and pattern of involvement could independently predict total scores of WOMAC, while age, marital status and pattern of involvement affected total score of SF-36. Conclusion: Pattern of involvement is a better predictor of quality of life than disease severity in patients with knee osteoarthritis.
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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 | 0.002 |
| 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 | 0.001 |
| 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".