SP734THE BREADTH AND CONSISTENCY OF OUTCOMES REPORTED IN RANDOMISED TRIALS CONDUCTED IN ADULT KIDNEY TRANSPLANT RECIPIENTS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Although kidney transplantation is the optimal treatment for many patients with end-stage kidney disease, long-term recipient outcomes have not substantially improved. This may be partly attributable to the heterogeneity of outcomes reported in trials, of which the majority may not be directly relevant to patients and clinicians. We aimed to assess the breadth and consistency of outcomes reported in trials in kidney transplantation. METHODS: We searched for the randomized trials conducted in adult kidney transplant recipients included in Cochrane systematic reviews to December 2015 and trials registered in ClinicalTrials.gov (2010-2015). We extracted all the outcomes, classified them into outcome domains and into clinical, surrogate or patient-reported outcome, then assessed the different measures and time points used. RESULTS: The 397 trials reported 12 047 outcomes measures and time points (median 19 per trial, IQR 9 to 42) across 106 different domains, of which 55 (52%) were surrogate, 35 (33%) were clinical and 16 (15%) were patient-reported. The top four most frequently reported outcome domains (and number of measures) were graft function (322 (81%) trials, 118 measures), acute graft rejection (234 [59%], 93 measures), graft loss (215 [54%], 48 measures) and mortality (204 [51%], 51 measures). Cancer, diabetes and cardiovascular disease were reported in 66 (17%), 104 (26%), and 59 (15%) trials, respectively. The 102 remaining outcomes domains were reported in less than 50% trials. The 16 patient-reported outcomes were reported in 10% or less trials. SP734 Figure
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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 | 0.255 | 0.570 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".