Spot urine protein measurements in kidney transplantation: a systematic review of diagnostic accuracy
Bibliographic record
Abstract
BACKGROUND: Quantification of proteinuria (albuminuria) in renal transplant recipients is important for diagnostic and prognostic purposes. Recent guidelines have recommended quantification of proteinuria by spot protein-to-creatinine ratio (PCR) or spot albumin-to-creatinine ratio (ACR). Validity of spot measurements remains unclear in renal transplant recipients. METHODS: Systematic review of adult kidney transplant recipients. Studies that reported the diagnostic accuracy of PCR or ACR as compared with 24-h urine protein or albumin excretion in renal transplant recipients were included. RESULTS: The search identified 8 studies involving 1871 renal transplant recipients. The correlation of the PCR to 24-h protein ranged from 0.772 to 0.998 with a median value of 0.92. PCR sensitivity ranged from 63 to 99 (50% of sensitivities were >90%); PCR specificity varied from 73 to 99 (50% of specificities were >90%). Only one study reported the bias; percent bias ranged from 12 to 21% and accuracy (within 30% of 24 h urine protein) ranged from 47 to 56% depending on the degree of proteinuria. For the ACR, percent bias ranged from 9 to 21%, and the accuracy (within 30%) ranged from 38 to 80%. CONCLUSIONS: The data regarding diagnostic accuracy of PCR and ACR is limited. Only one report studied the absolute measures of agreement (bias and accuracy). We recommend verifying PCR and ACR measurements with a 24-h protein before making any major diagnostic (e.g. biopsy) or therapeutic (e.g. change in immunosuppressive agents) decisions in this population.
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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 | 0.020 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".