Acute kidney injury in renal transplant recipients undergoing cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a key risk factor for chronic kidney disease in the general population, but has not been investigated in detail among renal transplant recipients (RTRs). We investigated the incidence, severity and risk factors for AKI following cardiac surgery among RTRs compared with non-RTRs with otherwise similar clinical characteristics. METHODS: We conducted a retrospective cohort study of RTRs (n = 83) and non-RTRs (n = 83) who underwent cardiac surgery at two major academic medical centers. Non-RTRs were matched 1:1 to RTRs by age, preoperative (preop) estimated glomerular filtration rate and type of cardiac surgery. We defined AKI according to Kidney Disease: Improving Global Outcomes criteria. RESULTS: RTRs had a higher rate of AKI following cardiac surgery compared with non-RTRs [46% versus 28%; adjusted odds ratio 2.77 (95% confidence interval 1.36-5.64)]. Among RTRs, deceased donor (DD) versus living donor (LD) status, as well as higher versus lower preop calcineurin inhibitor (CNI) trough levels, were associated with higher rates of AKI (57% versus 33% among DD-RTRs versus LD-RTRs; P = 0.047; 73% versus 36% among RTRs with higher versus lower CNI trough levels, P = 0.02). The combination of both risk factors (DD status and higher CNI trough level) had an additive effect (88% AKI incidence among patients with both risk factors versus 25% incidence among RTRs with neither risk factor, P = 0.004). CONCLUSIONS: RTRs have a higher risk of AKI following cardiac surgery compared with non-RTRs with otherwise similar characteristics. Among RTRs, DD-RTRs and those with higher preop CNI trough levels are at the highest risk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".