Incidence, etiology, and significance of acute kidney injury in the early post‐kidney transplant period
Bibliographic record
Abstract
Little is known about the incidence, causes, and significance of acute kidney injury (AKI) in the early transplant period. This study used a definition as >26 μmol/L increase in creatinine within 48 h or >50% increase over a period >48 h. In 326 adult consecutive recipients of a solitary kidney transplant from 2006 to 2014 followed at this center, 21% developed AKI within the first six months. Most etiologies were CNI toxicity (33%) or unknown (26%), whereas acute rejection accounted for 17% and urinary tract obstruction for 10%. Those with AKI had a significantly lower glomerular filtration rate (GFR) at one-yr post-transplant (adjusted beta coefficient -5.5 mL/min/1.73 m(2) , 95% CI: -10.4, -0.7, p = 0.025) in a multivariable linear regression model. However, the AKI definition missed 6 of 19 episodes of acute rejection and 4 of 10 episodes of urinary tract obstruction. When acute rejection (including those that did not satisfy AKI criteria) was included in the model, other causes of AKI were not significantly associated with GFR at year 1. Although AKI, using current criteria, is likely to be a significant predictor of later outcomes, important causes are missed and the criteria are not sensitive for clinical decision-making.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".