The lupus impact tracker is responsive to changes in clinical activity measured by the systemic lupus erythematosus responder index
Bibliographic record
Abstract
Objective The lupus impact tracker (LIT) is a 10-item patient reported outcome tool to measure the impact of systemic lupus erythematosus or its treatment on patients' daily lives. Herein, we describe the responsiveness of the LIT and LupusQoL to changes in disease activity, using the systemic lupus erythematosus responder index (SRI). Methods A total of 325 adult systemic lupus erythematosus patients were enrolled in an observational, longitudinal, multicentre study, conducted across the USA and Canada. Data (demographics, LIT, LupusQoL, BILAG, SELENA-SLEDAI) were obtained three months apart. Modified SRI was defined as: a decrease in SELENA-SLEDAI (4 points); no new BILAG A, and no greater than one new BILAG B; and no increase in the physician global assessment. Standardised response mean and effect size for LIT and LupusQoL domains were calculated among SRI responders and non-responders. Wilcoxon's test was used to compare the LIT and LupusQoL variation by SRI responder status. Results Of the participants 90% were women, 53% were white, 33% were of African descendant and 17% were Hispanic. Mean (SD) age and SELENA-SLEDAI at baseline were 42.3 (16.2) years and 4.3 (3.8), respectively. Mean (SD) LIT score at baseline was 39.4 (22.9). LIT standardised response mean (effect size) among SRI responders and non-responders were -0.69 (-0.36) and -0.20 (-0.12), respectively ( P = 0.02). For LupusQoL, two domains were responsive to SRI: standardised response mean (effect size) for physical health and pain domains were 0.42 (0.23) and 0.65 (0.44), respectively. Conclusions LIT is moderately responsive to SRI in patients with systemic lupus erythematosus. Inclusion of this tool in clinical care and clinical trials may provide further insights into its responsiveness. This is the first systemic lupus erythematosus patient reported outcome tool to be evaluated against composite responder index (SRI) used in clinical trials.
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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 | 0.007 | 0.016 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".