Validity and reliability of patient reported outcomes measurement information system computerized adaptive tests in systemic lupus erythematous
Bibliographic record
Abstract
BACKGROUND: The evaluation of Patient Reported Outcomes Measurement Information System (PROMIS) computerized adaptive test (CAT) in adults with systemic lupus erythematous (SLE) is an emerging field of research. We aimed to examine the test-retest reliability and construct validity of the PROMIS CAT in a Canadian cohort of patients with SLE. METHODS: Two hundred twenty-seven patients completed 14 domains of PROMIS CAT and seven legacy instruments during their clinical visits. Test-retest reliability of PROMIS was evaluated 7-10 days from baseline using intraclass correlation coefficient (ICC (2; 1)). The construct validity of the PROMIS CAT domains was evaluated against the commonly used legacy instruments, and also in comparison to disease activity and disease damage using Spearman correlations. A multitrait-multimethod matrix (MMM) approach was used to further assess construct validity comparing selected 10 domains of PROMIS and SF-36 domains. RESULTS: Moderate to excellent reliability was found for all domains (ICC [2;1] ranging from lowest, 0.66 for Sleep Disturbance and highest, 0.93 for the Mobility domain). Comparing seven legacy instruments with 14 domains of PROMIS CAT, moderate to strong correlations (0.51-0.91) were identified. The average time to complete all PROMIS CAT domains was 11.7 min. The MMM further established construct validity by showing moderate to strong correlations (0.55-0.87) between select PROMIS and SF-36 domains; the average correlations from similar traits (convergent validity) were significantly greater than the average correlations from different traits. CONCLUSIONS: These results provide evidence on the reliability and validity of PROMIS CAT in SLE in a Canadian cohort.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".