Minimal Detectable Changes of the Health Assessment Questionnaire–Disability Index, Patient‐Reported Outcomes Measurement Information System‐29 Profile Version 2.0 Domains, and Patient Health Questionnaire‐8 in People With Systemic Sclerosis: A Scleroderma Patient‐Centered Intervention Network Cohort Cross‐Sectional Study
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a rare, chronic autoimmune disorder associated with disability, diminished physical function, fatigue, pain, and mental health concerns. We assessed minimal detectable changes (MDCs) of the Health Assessment Questionnaire-Disability Index (HAQ-DI), Patient-Reported Outcomes Measurement Information System-29 Profile version 2.0 (PROMIS-29v2.0) domains, and Patient Health Questionnaire (PHQ)-8 in people with SSc. METHODS: Scleroderma Patient-Centered Intervention Network Cohort participants completed the HAQ-DI, PROMIS-29v2.0 domains, and PHQ-8 at baseline assessments from April 2014 until August 2023. We estimated MDC95 (smallest change that can be detected with 95% certainty) and MDC90 (smallest change that can be detected with 90% certainty) with 95% confidence intervals (CIs) generated via the percentile bootstrapping method resampling 1,000 times. We compared MDC estimates by age, sex, and SSc subtype. RESULTS: A total of 2,571 participants were included. Most were female (n = 2,241; 87%), and 38% (n = 976) had diffuse SSc. Mean (±SD) age was 54.9 (±12.7) years and duration since onset of first non-Raynaud phenomenon symptom was 10.8 (±8.7) years. MDC95 estimate was 0.41 points (95% CI 0.40-0.42) for the HAQ-DI, between 4.88 points (95% CI 4.72-5.05) and 9.02 points (95% CI 8.80-9.23) for the seven PROMIS-29v2.0 domains, and 5.16 points (95% CI 5.06-5.26) for the PHQ-8. MDC95 estimates were not materially different across subgroups. CONCLUSION: MDC95 and MDC90 estimates were precise and similar across age, sex, and SSc subtype groups. HAQ-DI MDC95 and MDC90 were substantially larger than previous estimates of HAQ-DI minimal important difference from several small studies. Minimally important differences of all measures should be evaluated in large studies using anchor-based methods.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".