Serum Albumin as a Marker for Disease Activity in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine whether serum albumin reflects disease activity in patients with systemic lupus erythematosus (SLE) with and without nephritis (LN, LNN), and whether serum albumin could be a surrogate marker of SLE disease activity overall. There is currently no clinical "gold standard" in the assessment of disease activity in SLE. METHODS: Patients with >or= 3 clinic visits within a maximum followup period of 10 years were selected from the University of Toronto Lupus Clinic database. Subjects were divided into 3 groups: LN-B, those with nephritis defined by histological findings on renal biopsies; LN-L, those with nephritis defined by laboratory abnormalities in the absence of biopsy; and LNN, those without nephritis. In a subanalysis, the renal groups were further stratified by proteinuria status. The associations of SLE-Disease Activity Index (SLEDAI-2K) with serum albumin and dsDNA were examined using the mixed model regression analysis. RESULTS: A total of 1078 patients were studied: 89.1% female, 71.5% white, mean age 33.6 (SD 12.6) years, and with median baseline SLEDAI-2K of 8. Serum albumin was more significantly associated with SLEDAI in LN-B and LN-L. The association was also present but weaker in the LNN group. In all LN, the associations between serum albumin and SLEDAI-2K were stronger in those with proteinuria. CONCLUSION: In patients with SLE, higher SLEDAI was associated with lower serum albumin levels.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".