Estimating glomerular filtration rate in patients with acute kidney injury: a prospective multicenter study of diagnostic accuracy
Bibliographic record
Abstract
BACKGROUND: Estimating glomerular filtration rate (GFR) in acute kidney injury (AKI) is challenging, with limited data comparing estimated and gold standard methods to assess GFR. The objective of our study was to assess the performance of the kinetic estimated GFR (KeGFR) and Jelliffe equations to estimate GFR in AKI, using a radioisotopic method (technetium-diethylenetriaminepentaacetic acid) as a reference measure. METHODS: We conducted a prospective multicenter observational study in hospitalized patients with AKI. We computed the Jelliffe and KeGFR equations to estimate GFR and compared these estimations to measured GFR (mGFR) by a radioisotopic method. The performances were assessed by correlation, Bland-Altman plots and smoothed and linear regressions. We conducted stratified analyses by age and chronic kidney disease (CKD). RESULTS: The study included 119 patients with AKI, mostly from the intensive care unit (63%) and with Stage 1 AKI (71%). The eGFR obtained from the Jelliffe and KeGFR equations showed a good correlation with mGFR (r = 0.73 and 0.68, respectively). The median eGFR by the Jelliffe and KeGFR equations was less than the median mGFR, indicating that these equations underestimated the mGFR. On Bland-Altman plots, the Jelliffe and KeGFR equations displayed a considerable lack of agreement with mGFR, with limits of agreement >40 mL/min/1.73 m2. Both equations performed better in CKD and the KeGFR performed better in older patients. Results were similar across AKI stages. CONCLUSIONS: In our study, the Jelliffe and KeGFR equations had good correlations with mGFR; however, they had wide limits of agreement. Further studies are needed to optimize the prediction of mGFR with estimatation equations.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".