Quantitative Data for Care of Patients with Systemic Lupus Erythematosus in Usual Clinical Settings: A Patient Multidimensional Health Assessment Questionnaire and Physician Estimate of Noninflammatory Symptoms
Bibliographic record
Abstract
OBJECTIVE: To analyze quantitative data in patients with systemic lupus erythematosus (SLE), seen in usual care, from a patient Multidimensional Health Assessment Questionnaire (MDHAQ) with routine assessment of patient index data (RAPID3) scores and from a physician global estimate of noninflammatory symptoms; and to compare results to self-report Systemic Lupus Activity Questionnaire (SLAQ) scores and 4 SLE indices: SLE Disease Activity Index-2K (SLEDAI-2K), British Isles Lupus Assessment Group (BILAG), Systemic Lupus Activity Measure (SLAM), and European Consensus Lupus Activity Measurement (ECLAM). METHODS: Fifty consecutive patients with SLE were studied in usual care of one rheumatologist. All patients completed an MDHAQ/RAPID3 in this setting. Each patient also completed a SLAQ. The rheumatologist scored SLEDAI-2K, BILAG, SLAM, ECLAM, and 2 physician global estimates, one for overall status and one for noninflammatory symptoms. Patients were classified into 2 groups: "few" or "many" noninflammatory symptoms. Scores and indices were compared using correlations, cross-tabulations and t tests. RESULTS: The patients included 45 women and 5 men. MDHAQ/RAPID3 and SLAQ scores were significantly correlated. RAPID3 scores were significantly higher in patients with SLE index scores above median levels, and in 34 patients scored by the rheumatologist as having "few" noninflammatory symptoms. MDHAQ/RAPID3 and SLAQ were significantly higher in 16 patients scored as having many noninflammatory symptoms. CONCLUSION: MDHAQ/RAPID3 and SLAQ subscale scores appear to reflect disease activity in patients with SLE, but not in patients with many noninflammatory symptoms. A physician scale for noninflammatory symptoms is useful to interpret MDHAQ/RAPID3, SLAQ, and SLE index scores.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".