Post-transplant nuclear renal scans correlate with renal injury biomarkers and early allograft outcomes
Bibliographic record
Abstract
BACKGROUND: Clinical- and histopathology-based scores are limited predictors of allograft outcome. In addition, more objective markers of early transplant function are needed to identify and validate biomarkers and predictive scores. We evaluated existing scores and transcriptome biomarkers of kidney injury as predictors of early transplant function measured by renal scan. METHODS: Clinical, histopathologic and transcriptome data were collected in 143 consecutive kidney transplant recipients. A post-operative renal scan was performed within 48 h. Prediction scores for early outcomes were calculated. RESULTS: Patients were stratified into three groups by renal scan: normal, mild-to-moderate or severe dysfunction. Kidneys with severe dysfunction were more often from deceased donors (P < 0.001), had greater HLA antigen mismatches (P < 0.001), were transplanted into older recipients (P = 0.040), had lower urine output during the first 8 h (P < 0.001), higher Day 7 serum creatinine (P < 0.001) and higher incidence of delayed graft function (P < 0.001). Clinical- and pathology-based scores did not discriminate between scan groups. In contrast, the overall transcriptome (P < 0.001) and transcripts of preselected acute kidney injury (AKI) genes were significantly different between the groups, with kidney injury molecule 1 (P = 0.001) and neutrophil gelatinase-associated lipocalin (P = 0.002) being most highly expressed and genes associated with glutathione metabolism (GSTA1, 3 and 4) most down-regulated in kidneys with subsequent severe dysfunction. CONCLUSIONS: Renal scans reflect early transplant function and allow for a more objective assessment of scores predicting early outcome and for identification of biomarkers. The study shows that transcript levels of AKI genes correlate better with renal scans than clinical- or histopathology-based scores.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".