Predictive role of Oxford Classification for prognosis in children with IgA nephropathy: a systematic review and meta-analysis
Bibliographic record
Abstract
Background The Oxford Classification was proposed as an independent prognostic indicator in IgA nephropathy (IgAN). However, most studies on the subject focus on adults instead of children.Objectives Using a meta-analysis to appraise the predictive roles of the Oxford classification for the prognosis of pediatric patients with IgAN.Methods All cohort studies regarding the analysis of the association between poor kidney-related prognosis (GFR categories G2-G5) according to the Kidney Disease Improving Global Outcomes (KDIGO) Guideline in pediatric patients with IgAN and five pathologic lesions in the Oxford Classification were included. Hazard ratios (HRs) regarding the association between the Oxford classification and prognosis of pediatric patients with IgAN were synthesized using random effect models. The risk of bias in studies was assessed based on the Newcastle-Ottawa scale.Results Fourteen articles were included with 5679 IgAN patients and 710 endpoint outcome events occurred. M1 was associated with a higher risk of poor kidney-related prognosis compared with M0, pooled HR (1.79; 95%CI, 1.46–2.19; p < 0.001, random effect model). S1 and T1 or T2 increased the risk of poor kidney-related prognosis (pooled HR, 2.13; 95%CI, 1.68–2.70; p < 0.001; pooled HR, 2.64; 95%CI, 1.81–3.86; p < 0.001, respectively, estimated by random effect model). Compared with C0, C1, or C2 was also associated with an increased risk of poor kidney-related prognosis in the subgroup analysis of Asian and other populations. Evidence to indicate that E1 increased the risk of poor kidney-related prognosis was marginal.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".