Potential Advantages and Limitations of Applying the Chronic Kidney Disease Classification to Kidney Transplant Recipients
Bibliographic record
Abstract
The National Kidney Foundation (NKF) Kidney Disease Outcomes Quality Initiative (K/DOQI) classification of Chronic Kidney Disease (CKD) characterizes patients by their level of kidney function and includes kidney transplant recipients (KTRs). Most KTRs have stage > or = 3 CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2) and may benefit from aggressive CKD care. Recent modifications to the K/DOQI CKD classification reflect the recognition of KTRs as a unique subset of CKD patients in whom the presentation, progression and implications of CKD may vary from those in nontransplant CKD populations. Currently, there is limited information about how adopting the CKD classification in KTRs will influence clinical management and outcomes. Appropriately designed studies are needed to develop transplant-specific CKD treatment recommendations, and to ensure patient, health provider and payer acceptance of the continued need for aggressive CKD care after transplantation. Education and implementation strategies will be required to ensure appropriate integration of the CKD classification and treatment guidelines into existing posttransplant care programs. The CKD classification thus represents an exciting potential strategy to improve clinical outcomes that should be adopted, further studied and modified to incorporate considerations unique to KTRs.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".