The Change in Allograft Function among Long-Term Kidney Transplant Recipients
Bibliographic record
Abstract
Long-term kidney allograft survival continues to remain an elusive goal. Kidney transplant recipients are believed to be at high risk for loss of allograft function, and new, potentially non-nephrotoxic immunosuppressive medications are advocated to improve long-term allograft survival. To evaluate the efficacy of such therapeutic interventions, information regarding the change in GFR among kidney transplant recipients with long-term allograft survival is needed. We studied 40,963 transplant recipients between 1987 and 1996 with allograft survival of at least 2 yr in the United States Renal Data System. Linear regression methods were applied to serial GFR estimates after transplantation. The baseline mean GFR at 6 mo after transplantation was 49.6 +/- 15.4 ml/min per 1.73 m(2). During the mean follow-up of 5.7 +/- 2.3 yr, the mean +/- standard error of the change in GFR was -1.66 +/- 6.51 ml/min per 1.73 m(2) per year (median, -0.94 L/min per 1.73 m(2) per year). A total of 12,583 (30%) of patients had improvement in GFR, 8133 (20%) patients had no change in GFR, and 20,247 (50%) patients had decline in GFR. It is concluded that, although most patients had significant impairment of GFR at baseline, the decline in GFR was slow and many patients had either no change or improvement in GFR. Strategies to improve long-term kidney allograft survival that increase baseline allograft function may be more effective than strategies to slow the decline in GFR.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".