SLEDAI-2K Responder Index 50 captures 50% improvement in disease activity over 10 years
Bibliographic record
Abstract
OBJECTIVES: To determine the frequency and the time to complete recovery identified by Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) and the time to partial recovery identified by the SLEDAI-2K Responder Index 50 (SRI-50) in three laboratory systems over 10 years. METHODS: This is a retrospective analysis of the data available from the Toronto Lupus Clinic over the last 10 years. Patients with SLEDAI-2K renal, immunological and hematologic active descriptors were identified. The percentage of descriptors with partial and complete recovery was studied at one year and over the study period. Descriptive analysis and the Kaplan-Meier estimator were applied to study the time to partial and complete recovery. RESULTS: Of the 795 patients, 94% had an active system at some point during the study period. Partial recovery was shown in 66% of patients by SRI-50 for at least one descriptor over the study period. None of these partial findings identified would have been captured using SLEDAI-2K alone. The time to partial recovery identified by SRI-50 was shorter than the time to complete recovery identified by SLEDAI-2K. CONCLUSION: The SRI-50 is a valid responder index derived form SLEDAI-2K and is very helpful in identifying clinically important improvement in active laboratory descriptors in an efficient time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".