Burden of autoantibodies and association with disease activity and damage in systemic lupus erythematosus.
Bibliographic record
Abstract
OBJECTIVES: To determine whether immunological burden of autoantibodies as reflected by the number of cumulative antibodies present at inception and after 3 and 5 years is associated with or predicts subsequent disease activity and damage in lupus. METHODS: Patients with SLE followed from inception at a single centre between 1992 and 2007 were included. Twelve autoantibodies were assayed in each patient at years 1, 3 and 5 of disease. The relationship between the burden of autoantibodies and outcomes, SDI (Systemic Lupus International Collaborative Clinics Damage Index), AMS (Adjusted Mean SLEDAI-2K) and AMS excluding anti-ds DNA (AMS-DNA) was evaluated as an association and as prediction. We determined the association between autoantibody burden and outcomes at years 1, 3 and 5 and the prediction using autoantibody burden at year 1 and year 3 to predict outcomes at years 3 and 5 respectively. RESULTS: Between 1992 and 2007, 235 inception patients were identified. Of these, 223, 163 and 129 patients had 10 or more autoantibodies tested at years 1, 3 and year 5 following diagnosis respectively. There was no association between the burden at years 1, 3 and 5 and outcome measures at years 1, 3 and 5 respectively. Furthermore, burden of autoantibodies at years 1 and 3 did not predict the outcome measures at years 3 and 5 respectively. CONCLUSIONS: Immunological burden in SLE at years 1, 3 or 5 as reflected by the number of autoantibodies found, was not associated with or predictive of subsequent disease activity or damage over time.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".