Development of clinical and laboratory biomarkers in an international cohort of 428 children with lupus nephritis
Bibliographic record
Abstract
Abstract Background There is no consensus on which treatment goals should be achieved to protect kidney function in children with lupus nephritis (LN). Methods We retrospectively analyzed trends of commonly used laboratory biomarkers of 428 patients (≤ 18 years old) with biopsy proven LN class ≥ III diagnosed and treated in the last 10 years in 25 international centers. We compared data of patients who developed stable kidney remission from 6 to 24 month with those who did not. Results Twenty five percent of patients maintained kidney stable remission while 75% did not. Significantly more patients with stable kidney remission showed normal hemoglobin and erythrocyte sedimentation rate values from 6 to 24 months compared to the group without stable kidney remission. Normal kidney function at onset, eGFR ≥90 ml/min/1.73m2, predicted the development of stable kidney remission (93.8%) compared to 64.7% in those without stable remission (P< 0.00001). At diagnosis 5.9% and 20.2% of the patients showed no proteinuria in the group with and without stable kidney remission respectively (P 0.0001). DsDNA antibodies decreased from onset of treatment mainly during the first 3 months in all the groups, but more than 50% of all patients in both groups never normalized after 6 months. Complement C3 and C4 increased mainly in the first three months in all the patients without any significant difference. Conclusion Normal eGFR and the absence of proteinuria at onset and the normalization of Hb and ESR from 6 to 24 month were predictors of stable kidney remission.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".