Faculty Opinions recommendation of Urinary EGF and MCP-1 and risk of CKD after cardiac surgery.
Bibliographic record
Abstract
BACKGROUND: Assessment of risk for chronic kidney disease (CKD) after acute kidney injury (AKI) is based on a limited set of markers primarily reflecting glomerular function. We evaluated markers of cell integrity (EGF) and inflammation (monocyte chemoattractant protein-1 [MCP-1]) for predicting long-term kidney outcomes after cardiac surgery.METHODS: We measured the urinary biomarkers EGF and MCP-1 in pre- and post-operative urine samples from 865 adult patients who underwent cardiac surgery from 2007-2010 at 2 sites in Canada and the United States and assessed their associations with the composite outcome of CKD incidence or progression. We also used single-cell (Sc) RNAseq of biopsies from patients with AKI to perform a transcriptomic analysis of programs that are coregulated with the genes encoding the 2 biomarkers.RESULTS: Over a median (IQR) follow-up of 5.8 (4.2-7.1) years, 266 (30.8%) patients developed the composite CKD outcome. Post-operatively, higher levels of urinary EGF were protective and higher levels of MCP-1 were associated with the composite CKD outcome (adjusted HR 0.83, 95% CI 0.73-0.95 and 1.10, 95% CI 1.00-1.21, respectively). Intrarenal scRNAseq transcriptomes in patients with AKI-defined cell populations revealed concordant changes in EGF and MCP-1 levels and identified underlying molecular processes associated with loss of EGF expression and gain of CCL2 (encoding MCP-1) expression.CONCLUSION: Urinary EGF and MCP-1 were each independently associated with CKD incidence or progression after cardiac surgery. These markers may serve as noninvasive indicators of tubular damage, supported by tissue transcriptomes and provide opportunity for novel interventions in cardiac surgery.TRIAL REGISTRATION: ClinicalTrials.gov NCT00774137FUNDING. NIH (R01HL085757 to CRP) funded the TRIBE-AKI Consortium. PMID: 33974569
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.428 | 0.273 |
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".