Adjusted mean Systemic Lupus Erythematosus Disease Activity Index-2K is a predictor of outcome in SLE.
Bibliographic record
Abstract
OBJECTIVE: To test the predictability of the adjusted mean Systemic Lupus Erythematosus Disease Activity Index-2K (AMS) for main outcomes in systemic lupus erythematosus (SLE), namely presence of damage, coronary artery disease (CAD), and avascular necrosis (AVN). METHODS: Included in this study are patients with regular followup from the University of Toronto Lupus Clinic. This was defined as a minimum of 3 visits and no absence exceeding 18 consecutive months. For each visit, AMS was evaluated. The ability of the AMS to predict each of the main outcomes was evaluated through time-dependent covariate survival analysis. Adjustments to the regression models were made to include other risk factors such as sex, age at diagnosis (AGE), SLEDAI-2K at presentation (SLEDAI), disease duration (DD), and use of corticosteroids, immunosuppressives (IM), or antimalarials (AM). RESULTS: Five hundred and seventy-five patients were included covering the period from 1970 to 2002. A total of 325 developed damage, 55 had CAD, and 68 had AVN. Presence of damage was not associated with sex, SLEDAI, or AM but was significantly associated with AMS, AGE, DD, and use of steroids or IM (all p < 0.001). CAD was not associated with SLEDAI or use of steroids or AM but with all other variables AMS (p = 0.046), sex (p = 0.009), AGE (p < 0.0001), DD (p < 0.0001), and IM (p = 0.035). Predictors of AVN were DD (p = 0.032) and IM (p < 0.0001) but not sex, AGE, use of steroids, AM, SLEDAI, or AMS. CONCLUSION: AMS is associated with the presence of damage and CAD. It is not associated with AVN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".