Introduction to the Well-Transplant Visit—More than Vital Signs and a Creatinine Check
Bibliographic record
Abstract
Against the backdrop of remarkable advances in renal transplantation in the past half-century resulting in excellent early patient and allograft survival rates, it is clear that commensurate improvement in long-term outcomes lags. Current research is increasingly focused on improving the longevity and quality of life after transplantation. As prognostic risk factors are identified, new strategies aimed at modifying their impact will be developed and tested. After a quarter century devoted to understanding calcineurin inhibitor nephrotoxicity as a major component of long-term allograft dysfunction, it is now obvious that many phenotypes of graft injury compromise long-term success. Most of the mechanisms are immune mediated. Proteinuria, independent of underlying pathogenesis or histology, is an important marker for an allograft at risk for failure. With successful management of anemia in patients with chronic kidney disease, approaches to correction of anemia after transplantation are the subject of increasing interest. Kidney transplant recipients are a diverse group whose heterogeneity underscores the need for careful consideration of novel approaches to reduce morbidity in the patient and maintain function in the allograft. At the most recent annual meeting of the American Society of Nephrology, a clinical conference was dedicated to discussion of the long-term treatment of clinically stable kidney transplant recipients, focusing on clinical approaches that are important in an outpatient setting. The symposium consisted of four presentations, each addressing a commonly encountered posttransplantation management conundrum: We thank the discussants for condensing their presentations into the summary manuscripts that follow and hope that the reader will find them informative for improving the long-term treatment of their kidney transplant recipients. Michelle A. Josephson, MD, University of Chicago Medical Center: “Monitoring and Managing Graft Health in the Kidney Transplant Recipient” Donald E. Hricik, MD, Case Western Reserve University School of Medicine: “Metabolic Syndrome in Kidney Transplantation: Management of Risk Factors” Greg A Knoll, MD, The Ottawa Hospital, Riverside Campus: “Posttransplantation Proteinuria: An Approach to Diagnosis and Management” Wolfgang C. Winkelmayer, MD, Stanford University School of Medicine: “Posttransplantation Anemia: Mechanisms and Management”
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.088 | 0.039 |
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".