Longitudinal Changes in Estimated and Measured GFR in Type 1 Diabetes
Bibliographic record
Abstract
Estimation of GFR from serum concentrations of creatinine and cystatin C has been refined using cross-sectional data from large numbers of people. However, the ability of the improved estimating equations to identify changes in GFR within individuals over time has not been rigorously evaluated, particularly within the normal range of GFR. In cross-sectional and longitudinal analyses of 1441 participants in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study with type 1 diabetes, we compared GFR estimated from creatinine (eGFR(Cr)), cystatin C (eGFR(Cys)), or both (eGFR(Cr+Cys)) with iothalamate GFR (iGFR), including changes in each over time. Mean (SD) iGFR was 122.7 (21.0) ml/min per 1.73 m(2). In cross-sectional analyses, eGFR(Cr+Cys) estimated iGFR with the highest correlation (r=0.48 versus 0.39-0.42), precision, and accuracy. In longitudinal analyses, change in eGFR(Cr+Cys) best estimated change in iGFR; however, differences between estimates were small, and no estimate accurately classified change in iGFR. Over a median 23 years of follow-up, mean rate of change in eGFR was similar across estimates of eGFR(Cr), eGFR(Cys), and eGFR(Cr+Cys) (-1.37, -1.11, and -1.29 ml/min per 1.73 m(2) per year, respectively). Associations of BP and hemoglobin A1c with change in eGFR were strongest for eGFR(Cys) and eGFR(Cr+Cys). Together, these results suggest that the addition of cystatin C to creatinine to estimate GFR may improve identification of the causes and consequences of GFR loss in type 1 diabetes, but may not meaningfully improve the tracking of GFR in clinical care.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".