Prognostic Value of Risk Score and Urinary Markers in Idiopathic Membranous Nephropathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Accurate prediction of prognosis may improve management of patients with idiopathic membranous nephropathy. This study compared the Toronto Risk Score and urinary low-molecular weight proteins. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: One hundred four patients with biopsy-proven idiopathic membranous nephropathy who presented between 1995 and 2008 with a well-preserved kidney function and nephrotic range proteinuria were included. Urinary β2-microglobulin and α1-microglobulin measurements were obtained by timed standardized measurements, and the Toronto Risk Score was calculated using data obtained from medical records. The endpoint was progression, which was defined as an increase in serum creatinine > 50% or > 25% with a concentration > 135 μmol/L. RESULTS: Forty-nine patients showed progression. The area under the receiver-operating characteristics curve was 0.78 (95% confidence interval = 0.69-0.88) for the risk score versus 0.80 (0.71-0.89) and 0.79 (0.71-0.88) for urinary β2- and α1-microglobulin, respectively. Differences were not significant. Persistent proteinuria did not add accuracy to the Toronto Risk Score. Conversely, its accuracy was not reduced when data from the first 6 months of follow-up were used. Furthermore, a score based on GFR estimated with the six-variable Modification of Diet in Renal Disease equation, calculated in the first 6 months of follow-up, gave an area under the receiver-operating characteristics curve of 0.83 (0.74-0.92), which was not statistically different from other markers. CONCLUSIONS: The prognostic accuracies of the Toronto Risk Score and urinary low-molecular weight proteins were not significantly different. The risk score can be calculated within 6 months of diagnosis, and a simplified risk score using estimated GFR-Modification of Diet in Renal Disease may be sufficient.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".