IgA Nephropathy in Czech Patients - Are We Able Reliably Predict the Outcome?
Bibliographic record
Abstract
BACKGROUND/AIMS: The aim of our study was to retrospectively analyse data of 520 Czech patients with IgA nephropathy (IgAN) and to specify the risk factors affecting renal survival of IgAN patients. METHODS: Cox proportional hazards regression model was used to evaluate the effects of different variables on renal survival during a median follow up of six years. McNemar´s test was used to analyse the progression of renal function according to Bartosik´s formula. RESULTS: In our retrospective analysis of 520 Czech IgAN patients Cox proportional hazards regression model with five variables [hypertension, sex, GFR, proteinuria, age] was used. Significant regression coefficient was found for GFR, hypertension and proteinuria. Using stepwise algorithm GFR (OR = 3.09), hypertension (OR = 2.09) and proteinuria (OR = 1.97) were found as the most important factors for renal survival in our group of IgAN patients. Among patients with CKD 3 we found significantly better renal survival in patients with proteinuria < 1g/day compared to patients with higher proteinuria. We did not find the significant difference between predicted progression of renal function due to Bartosik´s formula and real progression of renal parametres assessed by GFR at the end of the follow up in our group of IgAN patients. CONCLUSION: Our retrospective study of 520 Czech IgAN patients confirmed GFR, hypertension and proteinuria as the most important factors affecting the prognosis of IgAN patients. We validated Toronto Bartosik´s formula to predict prognosis of IgAN patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".