Prediction of Short- and Long-Term Outcomes in Childhood Nephrotic Syndrome
Bibliographic record
Abstract
INTRODUCTION: It is unknown whether steroid sensitivity and other putative risk factors collected at baseline can predict the disease course of idiopathic nephrotic syndrome in childhood. We determined whether demographic, clinical, and family reported factors at presentation can predict outcomes in idiopathic nephrotic syndrome. METHODS: An observational cohort of 631 children aged 1 to 18 years diagnosed with idiopathic nephrotic syndrome between 1993 and 2016 were followed up until clinic discharge, 18 years of age, end-stage kidney disease (ESKD), or the last clinic visit. Baseline characteristics were age, sex, ethnicity, and initial steroid sensitivity. Of these, 287 (38%) children also reported any family history of kidney disease, preceding infection, microscopic hematuria, and history of asthma/allergies. The outcomes were complete remission after initial steroid course, need for a second-line agent, frequently relapsing disease, and long-term remission. The discriminatory power of the models was described using the c-statistic. RESULTS: Overall, 25.7% of children had no further disease after their initial steroid course. In addition, 31.2% developed frequently relapsing disease; however, 77.7% were disease-free at 18 years of age. Furthermore, 1% of children progressed to ESKD. Logistic regression modeling using the different baseline exposures did not significantly improve the prediction of outcomes relative to the observed frequencies (maximum c-statistic, 0.63; 95% confidence interval [CI], 0.59-0.67). The addition of steroid sensitivity did not improve outcome prediction of long-term outcomes (c-statistic, 0.63; 95% CI, 0.54-0.70). CONCLUSIONS: Demographic, clinical, and family reported characteristics, specifically steroid sensitivity, are not useful in predicting relapse rates or long-term remission in idiopathic nephrotic syndrome. Further studies are needed to address factors that contribute to long-term health.
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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 | 0.000 |
| 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".