The Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC/ACR) Damage Index for Systemic Lupus Erythematosus International Comparison.
Bibliographic record
Abstract
OBJECTIVE: To compare patients with systemic lupus erythematosus (SLE) from different centers with respect to demographics and Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SLICC/ACR DI) scores, and to assess whether the SLICC/ACR DI changed over time, and whether initial DI scores were related to outcome. METHODS: Members of SLICC completed DI scores and patient demographics on patients followed in their centers. Information was provided at 2, 5-10, and > 10 years of followup. Data were entered on computer and analyzed on SPSS/PC+ and SAS using descriptive statistics and analysis of variance. RESULTS: Information for 1297 patients within 2 years of first clinic visit was submitted from 8 centers. There were 1187 women and 110 men with a mean age at diagnosis of SLE of 32 years. Seven hundred sixty-two were Caucasian, 423 were black, and the remainder were of other races. There were more blacks in the American centers than in Canadian or European centers. Five centers provided information for the 3 time periods. The DI increased over time. Ninety-nine patients had died. Higher SLICC/ACR DI scores were documented in patients who went on to die. CONCLUSION: The SLICC/ACR DI is a valid measure for damage 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".