MP822DEVELOPING CONSENSUS BASED PRIORITY OUTCOME DOMAINS FOR TRIALS IN KIDNEY TRANSPLANTATION: A MULTINATIONAL DELPHI SURVEY WITH PATIENTS, CAREGIVERS AND HEALTH PROFESSIONALS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: 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 (P/C) and health professionals (HP). METHODS: In a 3-round Delphi survey, P/C and HP rated the importance of outcome domains for trials in kidney transplantation on a 9-point Likert scale and provided comments. During Round 2 and 3, participants re-rated the outcomes after reviewing their own score, the distribution of the respondents’ scores, and comments. We calculated the median, mean, and proportion rating 7-9 (critically important), and analyzed comments thematically. RESULTS: 1018 participants (461 [45%] P/C and 557 [55%] HP) from 79 countries completed Round 1, and 779 (77%) completed Round 3. The top eight outcomes that met the consensus criteria in Round 3 (mean ≥7.5, median ≥8 and proportion >85%) in both groups were graft loss, graft function, chronic rejection, acute rejection, mortality, infection, cancer (excluding skin) and cardiovascular disease. Compared with HP, P/C gave higher priority to six outcomes (mean difference of 0.5 or more): skin cancer, surgical complications, cognition, blood pressure, depression, and ability to work. We identified five 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. The 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 | 0.165 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".