The Modification of Diet in Renal Disease (MDRD) Equations Provide Valid Estimations of Glomerular Filtration Rates in Patients with Advanced Heart Failure
Bibliographic record
Abstract
BACKGROUND: Glomerular filtration rate (GFR) has major prognostic implications in heart failure. Our objective was to validate the MDRD prediction equations for GFR in patients with advanced heart failure, and to compare their predictive performance to that of the Cockcroft-Gault (CG) equation. METHODS: We analysed GFR in 45 patients referred for heart transplantation evaluation. 51Cr-EDTA-measured GFR was compared to GFR estimates obtained by MDRD1 and MDRD2 equations, CG equation using actual body weight, and ideal body weight. Regression analyses and Pearson correlations were performed, and Bland and Altman plots were drawn. ROC curves were obtained to illustrate each equation's ability to predict a GFR less than 60 ml/min/1.73 m2 (moderate renal impairment). RESULTS: Patients had a mean age of 52 years, and 69% were in NYHA class III. The mean EDTA-measured GFR was 46.9+/-17.2 ml/min/1.73 m2. The MDRD1 equation provided the best predictive model (narrowest limits of agreement; r = 0.766, p < 0.001), and the highest performance in predicting a GFR less than 60 ml/min/1.73 m2 (area under curve: 0.901). CONCLUSIONS: MDRD equations, especially MDRD1, adequately predict GFR in advanced heart failure, with higher accuracy than the CG equation. MDRD1 also has higher performance in predicting a GFR less than 60 ml/min/1.73 m2.
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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 | 0.001 | 0.001 |
| 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".