The osteoarthritis knowledge scale
Bibliographic record
Abstract
OBJECTIVE: Accurate knowledge is central to effective self-care of osteoarthritis (OA). This study aimed to assess the measurement properties of the Osteoarthritis Knowledge Scale (OAKS) with versions for the hip and knee. METHODS: Participants with hip OA (n = 144), knee OA (n = 327), and no OA (n = 735) were recruited. Rasch analysis was conducted to assess psychometric properties using data from all participants with hip OA and 144 randomly selected participants with either knee OA or no OA. Test-retest reliability and measurement error were estimated among those with hip (n = 51) and knee (n = 142) OA. RESULTS: Four items from the draft scales were deleted following Rasch analysis. The final 11-item OAKS was unidimensional. Item functioning was not affected by gender, age, educational level, or scale version (hip or knee). Person separation index was 0.75. Test-retest intraclass correlation coefficient was 0.81 (95% CI 0.74, 0.86; hip version 0.66 [0.47, 0.79]; knee version 0.85 (0.79, 0.90)). Smallest detectable change was 9 points (scale range 11-55; hip OA version 11 points; knee OA version 8 points). CONCLUSION: The OAKS is a psychometrically adequate, unidimensional measure of important OA knowledge that can be used in populations with and without hip and knee OA. Caution is needed when using with populations with only hip OA as test-retest reliability of the hip version did not surpass the acceptable range.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".