SP564COPMARISON OF IMMUNOSUPPRESSIVE MEDICATION PRESCRIBED AMONG HEMODIALYSIS PATIENTS WITH FAILED KIDNEY TRANSPLANTS IN EUROPE, AUSTRALIA & NEW ZEALAND, AND NORTH AMERICA, FROM THE DIALYSIS OUTCOMES AND PRACTICE PATTERNS STUDY
Bibliographic record
Abstract
Introduction and Aims: Kidney transplant recipients who experience transplant failure and transition to dialysis typically are discontinued from their immunosuppressive medications (IM). However, the optimal timing of discontinuation is unknown and practices vary. Early IM discontinuation is common with the intention of avoiding infections and other adverse effects from IM use; whereas, later IM discontinuation may prevent human leukocyte antigen antibody sensitization and need for graft nephrectomy. We compared IM use in hemodialysis (HD) patients who had a transplant failure in 11 countries on three continents, using data from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Methods: DOPPS is a multinational prospective cohort study of hemodialysis patients ≥18 years old. Among DOPPS enrollees between 2005 and 2012 in Australia & New Zealand(ANZ), Belgium(Bel), Canada(Can), France(Fra), Germany(Ger), Italy(Ita), Spain(Spa), Sweden(Swe), United Kingdom(UK), and the United States(US), 1693 had a history of a failed kidney transplant and had information about medication prescriptions at study enrollment. The percent of patients receiving antimetabolites (azathioprine, mycophenolate mofetil, or mycophenolate sodium), calcineurin inhibitors (cyclosporin or tacrolimus), and oral corticosteroids (prednisone, prednisolone, or methylprednisolone) was calculated by country and by time since dialysis initiation following transplant failure (TF).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".