Summarizing disease features over time: II. Variability measures of SLEDAI-2K.
Bibliographic record
Abstract
OBJECTIVE: o determine if the variability of the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K), along with the Adjusted Mean SLEDAI-2K (AMS), can better predict major outcomes in SLE than the AMS alone. METHODS: Patients were followed in the Lupus Clinic at 2-6 month intervals. Clinical and laboratory information necessary to compute the SLEDAI-2K and Systemic Lupus International Collaborating Clinics/American College of Rheumatology damage index was collected prospectively and entered onto a computerized database. Patients followed for a minimum of 3 visits, and without absence for a period > 18 consecutive months, were included in the study. Six different approaches to measure variability of SLEDAI-2K were evaluated for each visit, along with AMS. Approaches were the standard deviation, the slope, average rate of change by visit, the range, the coefficient of variation, and the Percentage of the visits with a change in SLEDAI-2K > or = 3. The SLE outcomes under study were death, presence of damage, coronary artery disease (CAD), and osteonecrosis (ON). The predictability of each outcome was evaluated through time-dependent covariate survival analyses. Regression models included other known major risk factors such as sex, age at diagnosis, SLEDAI-2K at presentation, and disease duration. RESULTS: Five hundred seventy-five patients seen from 1970 to 2002 were included. The average time between visits was 4.0 +/- 2.2 months. Eighty-five patients died, 325 developed damage, 55 had CAD, and 68 had ON. None of the 6 variability measures added more statistical significance in the prediction of any of the 4 outcomes. For the prediction of survival, AMS [hazard ratio (HR) = 1.16, p < 0.0001] and age at diagnosis (HR 1.05, p < 0.0001) were the only significant risk factors. For presence of damage, AMS (HR 1.06, p < 0.0001), age at diagnosis (HR 1.02, p = 0.0004), and disease duration (HR 1.05, p < 0.0001) were predictors. CAD was predicted by AMS (HR 1.12, p = 0.0003), male sex (HR 2.31, p = 0.02), age at diagnosis (HR 1.06, p < 0.0001), and disease duration (HR 1.10, p < 0.0001). For ON, SLEDAI-2K at presentation (HR 1.04, p = 0.003) and disease duration (HR 0.92, p = 0.05) were significant risk factors. CONCLUSION: Multivariate analysis revealed that AMS, independent of variability of the SLEDAI-2K, is an important predictor of major outcomes in SLE.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".