Test-retest Reliability for HAQ-DI and SF-36 PF for the Measurement of Physical Function in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Due to no existing data, we aimed to derive evidence to support test-retest reliability for the Health Assessment Questionnaire-Disability Index (HAQ-DI) and 36-item Short Form Health Survey physical functioning domain (SF-36 PF) in psoriatic arthritis (PsA). METHODS: We identified datasets that collected relevant data for test-retest reliability for HAQ-DI and SF-36 PF, and evaluated them using Outcome Measures in Rheumatology (OMERACT) Filter 2.1 methodology. We calculated intraclass correlation coefficients (ICC) as a measure of test-retest reliability. We then conducted a quality assessment and evaluated the adequacy of test-retest reliability performance. RESULTS: Two datasets were identified for HAQ-DI and 1 for SF-36 PF in PsA. The quality of the datasets was good. The ICCs for HAQ-DI were good and excellent in study 1 (0.90, 95% CI 0.79-0.95) and study 2 (0.94, 95% CI 0.89-0.97). The ICC for SF-36 PF was excellent (0.96, 95% CI 0.92-0.98). The performance of test-retest reliability for both instruments was judged to be adequate. CONCLUSION: The new data derived support good and reasonable test-retest reliability for HAQ-DI and SF-36 PF in PsA.
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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 | 0.133 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".