Feasibility, Validity, and Reliability of the 10-item Patient Reported Outcomes Measurement Information System Global Health Short Form in Outpatients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility, validity, and reliability of the Patient Reported Outcomes Measurement Information System Global Health Short Form (PROMIS10) in outpatients with systemic lupus erythematosus (SLE). METHODS: SLE outpatients completed PROMIS10, Medical Outcomes Study Short Form-36 (SF-36), LupusQoL-US, and selected PROMIS computerized adaptive tests (CAT) at routine visits at an SLE Center of Excellence. Construct validity was evaluated by correlating PROMIS10 physical and mental health scores with PROMIS CAT, legacy instruments, and physician-derived measures of disease activity and damage. Test-retest reliability was determined among subjects reporting stable SLE activity at 2 assessments 1 week apart using intraclass correlation coefficients (ICC). RESULTS: A diverse cohort of 204 out of 238 patients with SLE (86%) completed survey instruments. PROMIS10 physical health scores strongly correlated with physical function, pain, and social health domains in PROMIS CAT, SF-36, and LupusQoL, while mental health scores strongly correlated with PROMIS depression CAT, SF-36, and LupusQoL mental health domains (Spearman correlations ≥ 0.70). Active arthritis, comorbid fibromyalgia (FM), and anxiety were associated with worse PROMIS10 scores, but sociodemographic factors and physician-assessed flare status were not. Test-retest reliability for PROMIS10 physical and mental health scores was high (ICC ≥ 0.85). PROMIS10 required < 2 minutes to complete. CONCLUSION: PROMIS10 is valid and reliable, and can efficiently screen for impaired physical function, pain, and emotional distress in outpatients with SLE. With strong correlations to LupusQoL and SF-36 but significantly reduced responder burden, PROMIS10 is a promising tool for measuring patient-reported outcomes in routine SLE clinical care and value-based healthcare initiatives.
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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 | 0.014 | 0.040 |
| 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 | 0.001 |
| 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".