Screening for proteinuria in kidney transplant recipients
Bibliographic record
Abstract
BACKGROUND: Proteinuria is a predictor of graft loss and death in kidney transplant recipients. This study examines the clinical significance of albumin-to-creatinine (ACR) and protein-to-creatinine (PCR) ratios compared with conventional dipstick measures of proteinuria. METHODS: At this single centre, 500 adult patients with a functioning kidney transplant > 4 months provided a urine sample for dipstick, ACR and PCR. The primary end point was defined as death-censored graft loss. Associations between proteinuria and graft loss were examined by concordance statistics and multivariate Cox models. RESULTS: There were 32 graft losses over a mean 2.98 years follow-up. PCR (c = 0.82, P < 0.001) and ACR (c = 0.83, P < 0.001) demonstrated similar concordance with events, and both scored higher than dipstick (c = 0.76, P < 0.001). ACR cut points of 30 and 300 mg/g for grading albuminuria were equivalent to 130 and 490 mg/g for PCR. Moderate grades of proteinuria by ACR and PCR were predicted of adverse events in a multivariate analysis. CONCLUSIONS: ACR and PCR are probably equivalent in predicting adverse events. Conventional dipstick is also predictive but does not appear to be as sensitive.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".