The Short Arthritis Assessment Scale: a brief assessment questionnaire for rapid evaluation of arthritis severity in research and clinical practice.
Bibliographic record
Abstract
OBJECTIVE: To develop a short, 4-item arthritis severity questionnaire that is simple to score, clinically useful and meaningful, and suitable for use in primary care, where osteoarthritis (OA) is the primary prevalent arthritis illness. METHODS: Data and items from the Medical Outcomes Study Short Form 36 (SF-36), the Western Ontario McMaster Osteoarthritis Index (WOMAC(c)), and visual analog scales (VAS) for pain and patient global severity were studied in 16,519 patients with arthritis. The Short Arthritis Assessment Scale (SAS) was developed by performing multivariable analyses that involved individually adding/subtracting items in differing regression models. The candidate items and scales were then studied by Rasch analysis, and tested for effect size, sensitivity to change, and reliability. The resultant scale was validated using data from a recent OA clinical trial. RESULTS: The VAS pain and VAS global severity scales and 2 items from the WOMAC in the VAS format, difficulty going down stairs and difficulty shopping, were found to be the best predictors of change in health status. The 4-item SAS was reliable (Cronbach's alpha = 0.87), demonstrated good test-retest reliability (Lin's concordance coefficient = 0.85), was unidimensional, and was strongly correlated with other important clinical measures, indicating good construct validity. Using data from a recent randomized clinical trial in OA, the SAS performed better than the WOMAC pain scale and the SF-36 physical component score in detecting change, and at least as well as the clinical trial VAS pain scale. CONCLUSION: The SAS is a 4-item arthritis severity questionnaire that can be easily administered in primary care for patients with OA, but is suitable for use across all arthritis illnesses. Scoring is simple, requiring only the addition of four 10-point scales, and interpretation is straightforward. The SAS may have a role in rapid assessment of the arthritis patient in primary care practice.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".