Utilizing Estimated Creatinine Excretion to Improve the Performance of Spot Urine Samples for the Determination of Proteinuria in Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Agreement between spot and 24-hour urine protein measurements is poor in kidney transplant recipients. We investigated whether using formulae to estimate creatinine excretion rate (eCER), rather than assuming a standard creatinine excretion rate, would improve the estimation of proteinuria from spot urine samples in kidney transplant recipients. METHODS: We measured 24 hour urine protein and albumin and spot albumin:creatinine (ACR) and spot protein:creatinine (PCR) in 181 Kidney transplant recipients." We utilized 6 different published formulae (Fotheringham, CKD-EPI, Cockcroft-Gault, Walser, Goldwasser and Rule) to estimate eCER and from it calculated estimated albumin and protein excretion rate (eAER and ePER). Bias, precision and accuracy (within 15%, 30% and 50%) of ACR, PCR, eAER, ePER were compared to 24-hour urine protein and albumin. RESULTS: ACR and PCR significantly underestimated 24-hour albumin and protein excretion (ACR Bias (IQR), -5.9 mg/day; p< 0.01; PCR Bias, (IQR), -35.2 mg/day; p<0.01). None of the formulae used to calculate eAER or ePER had a bias that was significantly different from the 24-hour collection (eAER and ePER bias: Fotheringham -0.3 and 7.2, CKD-EPI 0.3 and 13.5, Cockcroft-Gault -3.2 and -13.9, Walser -1.7 and 3.1, Goldwasser -1.3 and -0.5, Rule -0.6 and 4.2 mg/day respectively. The accuracy for ACR and PCR were lower (within 30% being 38% and 43% respectively) than the corresponding values estimated by utilizing eCER (for eAER 46% to 49% and ePER 46-54%). CONCLUSION: Utilizing estimated creatinine excretion to calculate eAER and ePER improves the estimation of 24-hour albuminuria/proteinuria with spot urine samples in kidney transplant recipients.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".