Developing Consensus-Based Priority Outcome Domains for Trials in Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Inconsistencies in outcome reporting and frequent omission of patient-centered outcomes can diminish the value of trials in treatment decision making. We identified critically important outcome domains in kidney transplantation based on the shared priorities of patients/caregivers and health professionals. METHODS: In a 3-round Delphi survey, patients/caregivers and health professionals rated the importance of outcome domains for trials in kidney transplantation on a 9-point Likert scale and provided comments. During rounds 2 and 3, participants rerated the outcomes after reviewing their own score, the distribution of the respondents' scores, and comments. We calculated the median, mean, and proportion rating 7 to 9 (critically important), and analyzed comments thematically. RESULTS: One thousand eighteen participants (461 [45%] patients/caregivers and 557 [55%] health professionals) from 79 countries completed round 1, and 779 (77%) completed round 3. The top 8 outcomes that met the consensus criteria in round 3 (mean, ≥7.5; median, ≥8; proportion, >85%) in both groups were graft loss, graft function, chronic rejection, acute rejection, mortality, infection, cancer (excluding skin), and cardiovascular disease. Compared with health professionals, patients/caregivers gave higher priority to 6 outcomes (mean difference of 0.5 or more): skin cancer, surgical complications, cognition, blood pressure, depression, and ability to work. We identified 5 themes: capacity to control and inevitability, personal relevance, debilitating repercussions, gaining awareness of risks, and addressing knowledge gaps. CONCLUSIONS: Graft complications and severe comorbidities were critically important for both stakeholder groups. These stakeholder-prioritized outcomes will inform the core outcome set to improve the consistency and relevance of 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 | 0.540 | 0.528 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".