Critically important outcomes for infection in trials in kidney transplantation: An international survey of patients, caregivers, and health professionals
Bibliographic record
Abstract
BACKGROUND: Infections are a common complication following kidney transplantation, but are reported inconsistently in clinical trials. This study aimed to identify the infection outcomes of highest priority for patients/caregivers and health professionals to inform a core outcome set to be reported in all kidney transplant clinical trials. METHODS: In an international online survey, participants rated the absolute importance of 16 infections and eight severity dimensions on 9-point Likert Scales, with 7-9 being critically important. Relative importance was determined using a best-worst scale. Means and proportions of the Likert-scale ratings and best-worst preference scores were calculated. RESULTS: 353 healthcare professionals (19 who identified as both patients/caregiver and healthcare professionals) and 220 patients/caregivers (190 patients, 22 caregivers, eight who identified as both) from 55 countries completed the survey. Both healthcare professionals and patients/caregivers rated bloodstream (mean 8.4 and 8.5, respectively; aggregate 8.5), kidney/bladder (mean 7.9 and 8.4; aggregate 8.1), and BK virus (mean 8.1 and 8.6; aggregate 8.3) as the top three most critically important infection outcomes, whilst infectious death (mean 8.8 and 8.6; aggregate 8.7), impaired graft function (mean 8.4 and 8.7; aggregate 8.5) and admission to the intensive care unit (mean 8.2 and 8.3; aggregate 8.2) were the top three severity dimensions. Relative importance (best-worst) scores were consistent. CONCLUSIONS: Healthcare professionals and patients/caregivers consistently identified bloodstream infection, kidney/bladder infections, and BK virus as the three most important infection outcomes, and infectious death, admission to intensive care unit and infection impairing graft function as the three most important infection severity outcomes.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".