Range and Consistency of Outcomes Reported in Randomized Trials Conducted in Kidney Transplant Recipients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The potential for clinical trials to impact patient care may be limited if the outcomes reported vary by trial and lack direct relevance to patients. Despite the many trials conducted in kidney transplantation, premature death due to cardiovascular disease, infection, and malignancy remains high. We aimed to assess the range and consistency of outcomes reported in trials in kidney transplantation. METHODS: We searched for randomized trials conducted in kidney transplantation. We extracted the outcome measures, classified them into outcome domains, and into categories (clinical, surrogate or patient-reported outcome [PRO]). We assessed the measures used for the top 4 domains. RESULTS: Overall, 397 trials reported 12 047 outcomes measures and time points (median, 19 per trial; interquartile range, 9-42) across 106 different domains, of which 55 (52%) were surrogate, 35 (33%) clinical, and 16 (15%) PRO. The 4 most frequently reported were graft function (322 [81%] trials, 118 outcome measures), acute rejection (234 [59%], 93 measures), graft loss (215 [54%], 48 measures), and mortality (204 [51%], 51 measures). The remaining 102 domains were reported in less than 50% of trials. CONCLUSIONS: Mortality- and graft-related outcome domains were frequently reported and assessed with a multiplicity of measures. Most outcome domains were surrogate outcomes, and the reporting of relevant life-threatening complications and PRO were uncommon. Establishing core outcomes based on the shared priorities of patients/caregivers and health professionals in kidney transplantation may improve the relevance and consistency of outcome reporting in trials to better inform clinical decision making.
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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 | 0.211 | 0.602 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".