Active lupus in Argentina: Results of a multicenter and national registry
Bibliographic record
Abstract
Objective To evaluate the association between patients’ characteristics and disease activity in an Argentine lupus registry. Methods Cross-sectional study. Disease activity was stratified into: Remission off-treatment: SLEDAI = 0, without prednisone and immunosuppressive drugs. Low disease activity Toronto Cohort (LDA-TC): SLEDAI ≤2, without prednisone or immunosuppressive drugs. Modified lupus low disease activity (mLLDAS): SLEDAI score of ≤4, with no activity in major organ systems and no new features, prednisone of ≤10 mg/day and/or immunosuppressive drugs (maintenance dose) and Active disease: SLEDAI score of >4 and prednisone >10 mg/day and immunosuppressive drugs. A descriptive analysis and logistic regression model were performed. Results A total of 1346 patients were included. Of them, 1.6% achieved remission off steroids, 0.8% LDA-TC, 12.1% mLLDAS and the remaining 85.4% had active disease. Active disease was associated with younger age ( p ≤ 0.001), a shorter time to diagnosis ( p ≤ 0.001), higher frequency of hospitalizations ( p ≤ 0.001), seizures ( p = 0.022), serosal disease ( p ≤ 0.001), nephritis ( p ≤ 0.001), higher SDI ( p ≤ 0.001), greater use of immunosuppressive therapies and higher doses of prednisone compared to those on mLLDAS. In the multivariable analysis, the variables associated with active disease were the presence of pleuritis (OR 2.1, 95% CI 1.2–3.9; p = 0.007), persistent proteinuria (OR 2.5, 95% CI 1.2–5.5; p ≤ 0.011), nephritis (OR 2.5, 95% CI 1.2–5.6; p = .018) and hospitalizations (OR 8.9, 95% CI 5.3–16.0; p ≤ 0.001) whereas age at entry into the registry was negatively associated with it (OR 0.9, 95% CI 0.9–1.0; p = 0.029). Conclusion Active disease was associated with shorter time to diagnosis, worse outcomes (SDI and hospitalizations) and renal, neurological and serosal disease.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".