Toward Establishing Core Outcome Domains For Trials in Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Treatment decisions in kidney transplantation requires patients and clinicians to weigh the benefits and harms of a broad range of medical and surgical interventions, but the heterogeneity and lack of patient-relevant outcomes across trials in transplantation makes these trade-offs uncertain, thus, the need for a core outcome set that reflects stakeholder priorities. METHODS: We convened 2 international Standardized Outcomes in Nephrology-Kidney Transplantation stakeholder consensus workshops in Boston (17 patients/caregivers; 52 health professionals) and Hong Kong (10 patients/caregivers; 45 health professionals). In facilitated breakout groups, participants discussed the development and implementation of core outcome domains for trials in kidney transplantation. RESULTS: Seven themes were identified. Reinforcing the paramount importance of graft outcomes encompassed the prevailing dread of dialysis, distilling the meaning of graft function, and acknowledging the terrifying and ambiguous terminology of rejection. Reflecting critical trade-offs between graft health and medical comorbidities was fundamental. Contextualizing mortality explained discrepancies in the prioritization of death among stakeholders-inevitability of death (patients), preventing premature death (clinicians), and ensuring safety (regulators). Imperative to capture patient-reported outcomes was driven by making explicit patient priorities, fulfilling regulatory requirements, and addressing life participation. Specificity to transplant; feasibility and pragmatism (long-term impacts and responsiveness to interventions); and recognizing gradients of severity within outcome domains were raised as considerations. CONCLUSIONS: Stakeholders support the inclusion of graft health, mortality, cardiovascular disease, infection, cancer, and patient-reported outcomes (ie, life participation) in a core outcomes set. Addressing ambiguous terminology and feasibility is needed in establishing these core outcome domains for trials in kidney transplantation.
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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 | 0.770 | 0.594 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.007 | 0.022 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".