Identifying a Response for the Systemic Lupus Erythematosus Disease Activity 2000 Glucocorticoid Index
Bibliographic record
Abstract
OBJECTIVE: To compare the performance of the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) and the SLEDAI-2K Glucocorticoids (SLEDAI-2KG) indices in identifying responders to standard of care therapy. METHODS: Data from adult patients seen between 1995 and 2018 at the University of Toronto Lupus Clinic were analyzed. Patients with active disease (SLEDAI-2K score ≥6) and receiving prednisone ≥5 mg/day, and with a follow-up visit at 9 months, were studied. Response to standard of care therapy at first follow-up visit was assessed using the SLEDAI-2K and SLEDAI-2KG. The performances of the SLEDAI-2K and SLEDAI-2KG were compared using a cutoff point of 4. RESULTS: In a cohort of 188, the majority of patients were female (86.0%) and White (47.9%). Of 188 patients, 145 (77.1%) were responders and had a decrease in SLEDAI-2K score of ≥4. The SLEDAI-2KG identified 142 (97.9%) responders among the SLEDAI-2K responders. More importantly, the SLEDAI-2KG identified 11 (25.6%) additional responders among SLEDAI-2K nonresponders (n = 43). This resulted from the ability of the SLEDAI-2KG to account for the decrease in glucocorticoids dose. CONCLUSION: The SLEDAI-2KG provides a novel concept for the assessment of lupus disease activity while accounting for glucocorticoids dosage to reflect on disease activity overall at a particular visit. The SLEDAI-2KG accounts for the disease activity for each descriptor while also accounting for the current glucocorticoids dosage. The SLEDAI-2KG adds 1 additional variable (glucocorticoids dosage) to the SLEDAI-2K, which could alter response rates in drug trials and observational studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".