Validity and Reliability of Patient Reported Outcomes Measurement Information System Computerized Adaptive Tests in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to assess the construct validity and the test-retest reliability of Patient Reported Outcomes Measurement Information System (PROMIS) computerized adaptive tests (CAT) in patients with systemic lupus erythematosus (SLE). METHODS: Adults with SLE completed the Medical Outcomes Study Short Form-36, LupusQoL-US version ("legacy instruments"), and 14 selected PROMIS CAT. Using Spearman correlations, PROMIS CAT were compared with similar domains measured with legacy instruments. CAT were also correlated with the Safety of Estrogens in Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) disease activity and the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) scores. Test-retest reliability was evaluated using ICC. RESULTS: There were 204 outpatients with SLE enrolled in the study and 162 completed a retest. PROMIS CAT showed good performance characteristics and moderate to strong correlations with similar domains in the 2 legacy instruments (r = -0.49 to 0.86, p < 0.001). However, correlations between PROMIS CAT and the SELENA-SLEDAI disease activity and SDI were generally weak and statistically insignificant. PROMIS CAT test-retest ICC were good to excellent, ranging from 0.72 to 0.88. CONCLUSION: To our knowledge, these data are the first to show that PROMIS CAT are valid and reliable for many SLE-relevant domains. Importantly, PROMIS scores did not correlate well with physician-derived measures. This disconnect between objective signs and symptoms and the subjective patient disease experience underscores the crucial need to integrate patient-reported outcomes into clinical care to ensure optimal disease management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".