Clinical and Serological Associations with the Development of Incident Proteinuria in Danish Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: In a longitudinal cohort study, we investigated whether clinical and serological manifestations at the time of classification of systemic lupus erythematosus (SLE) were predictive of subsequent development of incident proteinuria as a biomarker of incident lupus nephritis. METHODS: Patients fulfilling SLE classification criteria but having no proteinuria prior to or at the time of classification were included. Data on SLE manifestations, vital status, criteria-related autoantibodies, and SLE-associated medications were collected during clinical visits and supplemented by chart review. HR were calculated by Cox regression analyses. RESULTS: Out of 850 patients with SLE, 604 had not developed proteinuria at the time of SLE classification. Of these 604 patients, 184 (30%) developed incident proteinuria following SLE classification. The patients had a median followup of 11 years and 7 months. Younger age and history of psychosis at the time of classification were associated with development of incident proteinuria, just as were lymphopenia (HR 1.49, 95% CI 1.08-2.06), anti-dsDNA (HR 1.38, 95% CI 1.01-1.87), and a high number of autoantibodies (HR 1.26, 95% CI 1.06-1.48). CONCLUSION: The risk of incident proteinuria after onset of SLE was increased by the presence of lymphopenia, anti-dsDNA antibodies, psychosis, younger age, and a high number of autoantibodies at onset.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".