The health assessment questionnaire disability index and scleroderma health assessment questionnaire in scleroderma trials: An evaluation of their measurement properties
Bibliographic record
Abstract
OBJECTIVE: To evaluate the measurement properties of the Health Assessment Questionnaire (HAQ) disability index (DI) for group comparisons in scleroderma trials, and to determine if the Scleroderma Health Assessment Questionnaire (SHAQ) visual analog scales confer any measurement advantage over the HAQ DI. METHODS: A computer search for articles describing the use of the HAQ DI and SHAQ in scleroderma was performed. Evidence supporting the sensibility, reliability, validity, and responsiveness of these measures was evaluated. RESULTS: The SHAQ has incremental face and content validity over the HAQ DI because it addresses scleroderma-specific manifestations that also contribute to disability. The HAQ DI has good concurrent validity, construct validity, and predictive validity. Whether SHAQ confers incremental construct, concurrent, or predictive validity over the HAQ DI is uncertain. The HAQ DI appears more reliable than the SHAQ; however, reliability studies provide insufficient data to ascertain if minimum standards have been achieved. Responsiveness of the HAQ DI subscales has been demonstrated. CONCLUSION: The SHAQ has incremental face and content validity over the HAQ DI. The HAQ DI has greater reliability and demonstrated construct, concurrent, and predictive validity. Further investigation into the measurement properties of the HAQ DI and SHAQ visual analog scales, and their relation to the required standards of measurement is needed.
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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 | 0.219 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".