Urinary cell cycle arrest proteins for early prediction and phenotyping of subclinical and clinical acute kidney injury after liver transplantation
Bibliographic record
Abstract
Acute kidney injury (AKI) is common after liver transplantation, but difficult to diagnose with serum creatinine and urinary output. This study evaluated the early risk stratification capability of urinary tissue inhibitor of metalloproteinases-2 and insulin-like growth factor binding protein-7 (u[TIMP-2]*[IGFBP-7]) in a prospective adult liver transplantation cohort. u[TIMP-2]*[IGFBP-7] was measured 6 and 36 hours after graft reperfusion, with AKI and acute kidney disease diagnosed according to KDIGO and ADQI criteria at 7-day and 90-day windows. Subclinical AKI was defined as u[TIMP-2]*[IGFBP-7] >0.30 without clinical AKI. Among 78 included patients, AKI occurred in 45% (10.3%, 11.7%, and 23.4% for stages 1, 2, and 3). At 6 hours, 46% had u[TIMP-2]*[IGFBP-7] >0.30, predicting AKI (stage ≥1) with an OR of 3.23 ( p =0.01); at 36 hours, 37% had u[TIMP-2]*[IGFBP-7] >0.30, predicting stage 3 AKI with an OR of 4.22 ( p =0.009). Serum creatinine/urinary output criteria predicted AKI only in 10% and 18% at 6 and 36 hours, respectively. Subclinical patients with AKI (24%) had higher risks of acute kidney disease (42% vs. 26%), early allograft dysfunction (32% vs. 18%), graft loss (16% vs. 4%), and longer intensive care unit stays. u[TIMP-2]*[IGFBP-7] is a valuable biomarker for early AKI risk stratification after liver transplantation, with subclinical AKI representing a distinct, clinically relevant phenotype.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".