Standardized Outcomes in Nephrology-Transplantation: A Global Initiative to Develop a Core Outcome Set for Trials in Kidney Transplantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Although advances in treatment have dramatically improved short-term graft survival and acute rejection in kidney transplant recipients, long-term graft outcomes have not substantially improved. Transplant recipients also have a considerably increased risk of cancer, cardiovascular disease, diabetes, and infection, which all contribute to appreciable morbidity and premature mortality. Many trials in kidney transplantation are short-term, frequently use unvalidated surrogate endpoints, outcomes of uncertain relevance to patients and clinicians, and do not consistently measure and report key outcomes like death, graft loss, graft function, and adverse effects of therapy. This diminishes the value of trials in supporting treatment decisions that require individual-level multiple tradeoffs between graft survival and the risk of side effects, adverse events, and mortality. The Standardized Outcomes in Nephrology-Transplantation initiative aims to develop a core outcome set for trials in kidney transplantation that is based on the shared priorities of all stakeholders. METHODS: This will include a systematic review to identify outcomes reported in randomized trials, a Delphi survey with an international multistakeholder panel (patients, caregivers, clinicians, researchers, policy makers, members from industry) to develop a consensus-based prioritized list of outcome domains and a consensus workshop to review and finalize the core outcome set for trials in kidney transplantation. CONCLUSIONS: Developing and implementing a core outcome set to be reported, at a minimum, in all kidney transplantation trials will improve the transparency, quality, and relevance of research; to enable kidney transplant recipients and their clinicians to make better-informed treatment decisions for improved patient outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it