Classification of Kidney Transplant Recipients Using a Combination of Estimated GFR and Albuminuria Reflects Risk
Bibliographic record
Abstract
Background The 2012 Kidney Dialysis Initiative Global Outcomes chronic kidney disease (CKD) classification scheme subdivides stage 3 CKD and incorporates the urinary albumin-to-creatinine ratio (ACR). The aim of this study was to evaluate whether the novel scheme provides graded risk in kidney transplant recipients (KTRs). Methods Prevalent KTRs with available laboratory data were included. The primary outcome was a composite of doubling of serum creatinine, graft failure, or death. Patients were stratified using the CKD-Epidemiolgic Collaboration equation, and ACR and the event rate per 1000 patient-years in each CKD category were calculated. Results There were 269 KTRs with a mean follow-up of 4.5 ± 2.0 years. There was a graded increase in outcomes with increasing ACR and decreasing estimated glomerular filtration rate (eGFR). For the primary outcome, the event rate was 15.3 (95% confidence interval, 4.2-39.2) per 1000 patient-years for those with an eGFR greater than 60 mL/min per 1.73 m2 and an ACR less than 30 mg/g, whereas it was 375 (95% confidence interval, 193.8-655.1) for those with an eGFR less than 30 mL/min per 1.73 m2 and an ACR greater than 300 mg/g. Conclusions The novel Kidney Dialysis Initiative Global Outcomes classification scheme provides graded risk for important clinical events in KTRs. This information can be used to identify high-risk patients and to tailor follow-up and management strategies aimed at improving outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".