Validation of a systemic lupus activity questionnaire (SLAQ) for population studies
Bibliographic record
Abstract
The goal of this work was to develop an economical way of tracking disease activity for large groups of systemic lupus erythematosus (SLE) patients in clinical studies. A Systemic Lupus Activity Questionnaire (SLAQ) was developed to screen for possible disease activity using items from the Systemic Lupus Activity Measure (SLAM) and tested for its measurement properties. The SLAQ was completed by 93 SLE patients just prior to a scheduled visit. At the visit, a rheumatologist, blinded to SLAQ results, examined the subject and completed a SLAM. Associations among SLAQ, and SLAM (omitting laboratory items) and between individual items from each instrument were assessed with Pearson correlations. Correlations between pairs of instruments were compared using Student's t-tests. The mean score across all 24 SLAQ items was 11.5 (range 0-33); mean SLAM without labs was 3.0 (range 0-13). The SLAQ had a moderately high correlation with SLAM-nolab (r = 0.62, P < 0.0001). Correlations between patient-clinician matched pairs of items ranged from r = 0.06 to 0.71. Positive predictive values for the SLAQ ranged from 56 to 89% for detecting clinically significant disease activity. In studies of SLE, symptoms suggesting disease can be screened by self-report using the SLAQ and then verified by further evaluation.
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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 | 0.083 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".