The Patient Activity Scale-II Is a Generic Indicator of Active Disease in Patients with Rheumatic Disorders
Bibliographic record
Abstract
OBJECTIVE: To determine whether the Patient Activity Scale-II (PAS-II) is a generic measure of disease activity by assessing whether the relationship of PAS-II with treatment decision (indicating disease activity) is invariant across disease. METHODS: The Health Assessment Questionnaire-II (HAQ-II), a 10 cm visual analog scale for "pain," and another for "patient global assessment" were recorded from 1000 consecutive patients attending rheumatology outpatient clinics. Active disease was defined as treatment intensity increased and inactive disease was defined as treatment intensity unchanged or decreased. A logistic regression analysis was conducted with active disease as the dependent variable and the predictor variables were PAS-II, diagnostic category, and the interaction between diagnostic category and PAS-II. RESULTS: PAS-II had a weak but statistically significant association with active disease that was independent of diagnosis. An increase of 1 point in PAS-II increased the odds of being in the active disease state by 1.19 (95% CI 1.10 to 1.37). The relationship between active disease state and PAS was not affected by diagnostic category. CONCLUSION: PAS-II can be used as a generic self-report indicator of active disease across different rheumatic disorders, and not just in rheumatoid arthritis. The strength of the relationship with disease activity is weak and physician-derived indicators remain very important.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".