Development and Initial Validation of the Systemic Lupus Erythematosus Disease Activity Index 2000 Responder Index 50
Bibliographic record
Abstract
OBJECTIVE: To describe the development and validation of the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) Responder Index 50 (SRI-50), an index to measure improvement in disease manifestations on followup visits. METHODS: We proposed 50% improvement of SLEDAI-2K scores as this was felt by clinicians to reflect a clinically important improvement. We determined the best definitions of 50% improvement in each of the SLEDAI-2K descriptors. The SRI-50 data retrieval form was developed to standardize the documentation of the descriptors. The new assigned scores for the descriptors of SRI-50 were derived by dividing the score of SLEDAI-2K by 2. To evaluate the construct validity of SRI-50, all patients attending the Lupus Clinic from September 2009 to December 2009 were studied. Patients were assessed initially and on a followup visit according to both SLEDAI-2K and SRI-50 along with physician response assessment on a Likert scale (LS), which was considered the external construct. RESULTS: SRI-50 is a 2-page document comprising 24 descriptors. The scoring method is simple, intuitive, and cumulative, and can be derived during the patient visit. Of the 298 patients enrolled in this study, 141 had a followup visit and were studied further. SRI-50 scores decreased more in patients with LS ≥ 50% compared to LS < 50% with a decrease of > 3. The decrease in SRI-50 scores was statistically and clinically more significant than the decrease in SLEDAI-2K scores. SRI-50 detected incomplete improvement, which would not have been discerned using SLEDAI-2K. CONCLUSION: SRI-50 has construct validity and is able to demonstrate incomplete, but clinically significant, improvement in disease activity between visits in patients with lupus.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".