Application of the updated International IgA Nephropathy Prediction Tool in children one or two years post-biopsy
Bibliographic record
Abstract
The pediatric International IgA Nephropathy (IgAN) Prediction Tool comprises two models with and without ethnicity and is the first method to predict the risk of a 30% decline in estimated glomerular filtration rate (eGFR) or kidney failure in children at the time of biopsy using clinical risk factors and Oxford MEST histology scores. However, it is unknown if the Prediction Tool can be applied after a period of observation post-biopsy. Using an international multi-ethnic cohort of 947 children with IgAN, 38% of whom were followed into adulthood, the Prediction Tool was updated for use one-year after biopsy. Compared to the original pediatric Prediction Tool, the updated post-biopsy Prediction Tool had a better model fit with higher R 2 D (51%/50% vs 20%), significant increase in 4-year C-statistics (0.83 vs 0.73/0.69, ΔC 0.09 [95% confidence interval 0.07-0.10] and ΔC 0.14 [0.12-0.15]) and better 4-year calibration with lower integrated calibration indices (0.74/0.54 vs 2.45/1.01). Results were similar after internal validation and when the models were applied two-years after biopsy. Trajectories of eGFR after a baseline one-year post-biopsy were non-linear and those at higher predicted risk started with a lower eGFR and experienced a more rapid decline over time. In children, eGFR had a variable rate of increase until 15-18 years old then decreased linearly with a more rapid decline in higher risk groups that was similar to young adults of comparable risk. Thus, the original pediatric Prediction Tool should be used in children at the time of biopsy, and the updated pediatric Prediction Tool should be used to revaluate risk one- or two-years after biopsy.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".