Development and Validation of the Lupus Impact Tracker: A Patient‐Completed Tool for Clinical Practice to Assess and Monitor the Impact of Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To derive and validate a brief patient-completed instrument, the Lupus Impact Tracker (LIT), to assess and monitor the impact of systemic lupus erythematosus (SLE). METHODS: Items for the LIT were selected from the LupusPRO, a validated patient-reported outcomes measure, using 3 approaches: confirmatory factor analysis (CFA), stepwise regression, and patient focus groups. CFA was conducted to find items from the LupusPRO that fit a unidimensional structure to allow scoring as a single index. Stepwise regression methods identified items with the strongest relationship (convergent validity) with disease activity measures and patient health rating. Focus groups (n = 26 patients) identified the most important items describing SLE impact. Selected items were evaluated for reliability and validity. RESULTS: CFA found 21 items that fit a unidimensional structure. Stepwise regressions identified 15 of 21 items having good convergent validity with clinical measures. Patient focus groups identified 9 of 15 items as best capturing the impact of SLE. Overall, 7 items were selected across all 3 approaches (CFA, stepwise regression, and focus groups). Another 15 items were selected across 2 approaches. Through consensus with rheumatology clinician experts, a final set of 10 items was selected for the LIT. The LIT items showed good internal consistency (0.89) and test-retest reliabilities (0.87). Mean LIT scores differed significantly (P < 0.05) across criterion groups in the hypothesized direction, providing evidence of discriminant validity and responsiveness. CONCLUSION: The LIT is reliable and valid in SLE patients and offers a practical way for physicians and patients to assess and monitor the impact of SLE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".