Progress in Assessing Physical Function in Arthritis: PROMIS Short Forms and Computerized Adaptive Testing
Bibliographic record
Abstract
OBJECTIVE: Assessing self-reported physical function/disability with the Health Assessment Questionnaire Disability Index (HAQ) and other instruments has become central in arthritis research. Item response theory (IRT) and computerized adaptive testing (CAT) techniques can increase reliability and statistical power. IRT-based instruments can improve measurement precision substantially over a wider range of disease severity. These modern methods were applied and the magnitude of improvement was estimated. METHODS: A 199-item physical function/disability item bank was developed by distilling 1865 items to 124, including Legacy Health Assessment Questionnaire (HAQ) and Physical Function-10 items, and improving precision through qualitative and quantitative evaluation in over 21,000 subjects, which included about 1500 patients with rheumatoid arthritis and osteoarthritis. Four new instruments, (A) Patient-Reported Outcomes Measurement Information (PROMIS) HAQ, which evolved from the original (Legacy) HAQ; (B) "best" PROMIS 10; (C) 20-item static (short) forms; and (D) simulated PROMIS CAT, which sequentially selected the most informative item, were compared with the HAQ. RESULTS: Online and mailed administration modes yielded similar item and domain scores. The HAQ and PROMIS HAQ 20-item scales yielded greater information content versus other scales in patients with more severe disease. The "best" PROMIS 20-item scale outperformed the other 20-item static forms over a broad range of 4 standard deviations. The 10-item simulated PROMIS CAT outperformed all other forms. CONCLUSION: Improved items and instruments yielded better information. The PROMIS HAQ is currently available and considered validated. The new PROMIS short forms, after validation, are likely to represent further improvement. CAT-based physical function/disability assessment offers superior performance over static forms of equal length.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".