Longitudinal Evaluation of PROMIS-29 and FACIT-Dyspnea Short Forms in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To assess the sensitivity of the Patient-Reported Outcomes Measurement Information System 29-item Health Profile (PROMIS-29) and the Functional Assessment of Chronic Illness Therapy-Dyspnea 10-item short form (FACIT-Dyspnea) for measuring change in health status and dyspnea in systemic sclerosis (SSc). METHODS: One hundred patients with SSc completed the PROMIS-29, FACIT-Dyspnea, and traditional instruments [Medical Research Council Dyspnea Score, St. George's Respiratory Questionnaire (SGRQ), Health Assessment Questionnaire-Disability Index (HAQ-DI), and Medical Outcomes Study Short Form-36 (SF-36)] at baseline and 1-year visits. PROMIS-29, FACIT-Dyspnea, and traditional instrument change scores were compared across composite modified Medsger Disease Severity and modified Rodnan Skin score (mRSS) change groups. RESULTS: Moderately high Spearman correlation coefficients were observed between FACIT-Dyspnea and SGRQ (r = 0.57), FACIT-Dyspnea functional limitations and SF-36 physical component summary (PCS; r = 0.51), PROMIS-29 physical functioning and HAQ-DI (r = 0.50), and SF-36 PCS (r = 0.52) change scores. In most validity comparisons, PROMIS-29, FACIT-Dyspnea, HAQ-DI, and SF-36 scores performed similarly. While PROMIS-29 covers more content areas than SF-36 (e.g., sleep), it may do so at the expense of responsiveness of its 4-item physical function scale as compared to the multiitem-derived SF-36 PCS. Statistically significant increases in SF-36 role physical (p = 0.01) and physical component scale (p = 0.016), but not PROMIS-29, were observed in patients with mRSS improvement. CONCLUSION: PROMIS-29 and FACIT-Dyspnea are valid instruments to measure health status and dyspnea in patients with SSc. In physical function assessment, longer PROMIS short forms or computer adaptive testing should be considered to improve responsiveness to the effect of skin disease changes on physical function in patients with SSc.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".