A cross-sectional comparison of three self-reported functional indices in scleroderma
Bibliographic record
Abstract
OBJECTIVES: In scleroderma, outcome measures such as skin score provide only limited information about the functional impact of the disease. The requirement for validated and convenient instruments that reliably reflect disease morbidity is now recognized. This study compares the Disability Index of the Health Assessment Questionnaire (HAQ-DI) with two more recently developed scleroderma-specific tools: scleroderma-visual analogue scales (scleroderma-VAS) and the UK scleroderma Functional Score (UKFS). In addition, the use of clinical and laboratory measures as predictors of disease severity have been examined. METHODS: One hundred and fifteen consecutive patients were studied. Subjects completed the 20-item HAQ-DI, the scleroderma-VAS and a questionnaire related to hand and muscle function (UKFS). Clinical details, measurement of maximal hand-spread, fist-closure and investigations for internal organ involvement were recorded. RESULTS: Over 68% of patients with diffuse disease had moderate to severe disease on the UKFS, compared with 44% with limited disease. The mean UKFS in diffuse disease was 14.7 (s.d. 9.1) and 10.6 (s.d. 8.5) in the limited subset (P=0.02). The mean HAQ-DI in diffuse disease was 1.23 (s.d. 0.77) and 0.79 (s.d. 0.75) in the limited subset (P=0.005). The HAQ-DI showed significant correlation with UKFS (r=0.9; P < 0.001). Several clinical and laboratory measures were associated with higher HAQ-DI and UKFS. CONCLUSIONS: This is the first comparative study of the UKFS and the HAQ-DI. These data show a strong correlation between assessment methods. Higher scores correlated with clinical and laboratory indicators of severe disease. Used together, these inexpensive tools assess general and organ-specific symptoms, as well as functional limitation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".