Impact of Outcome Adjudication in Kidney Disease Trials: Observations From the Study of Heart and Renal Protection
Bibliographic record
Abstract
Introduction: We aimed to assess opportunities for trial streamlining and the scientific impact of adjudication on kidney and cardiovascular outcomes in CKD. Methods: We analysed the effects of adjudication of ~2100 maintenance kidney replacement therapy (KRT) and ~1300 major atherosclerotic events (MAEs) recorded in SHARP. We first compared outcome classification before versus after adjudication, and then re-ran randomised comparisons using pre-adjudicated follow-up data. Results: For maintenance KRT, adjudication had little impact with only 1% of events being refuted (28/2115). Consequently, randomised comparisons using pre-adjudication reports found almost identical results (pre-adjudication: simvastatin/ezetimibe 1038 vs placebo 1077; risk ratio [RR] 0.95, 95%CI 0.88-1.04; post-adjudicated: 1057 vs 1084; RR=0.97, 95%CI 0.89-1.05). For MAEs, about one-quarter of patient reports were refuted (324/1275 [25%]), and reviewing 3538 other potential vascular events and death reports identified only 194 additional MAEs. Nevertheless, randomised analyses using SHARP's pre-adjudicated data alone found similar results to analyses based on adjudicated outcomes (pre-adjudication: 573 vs 702; RR=0.80, 95%CI 0.72-0.89; adjudicated: 526 vs 619; RR=0.83, 95%CI 0.74- 0.94), and also suggested refuted MAEs were likely to represent atherosclerotic disease (RR for refuted MAEs=0.80, 95%CI 0.65-1.00). Conclusions: These analyses provide three key insights. First, they provide a rationale for nephrology trials not to adjudicate maintenance KRT. Secondly, when an event that mimics an atherosclerotic outcome is not expected to be influenced by the treatment under study (e.g. heart failure), the aim of adjudicating atherosclerotic outcomes should be to remove such events. Lastly, restrictive definitions for the remaining suspected atherosclerotic outcomes may reduce statistical power.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.498 | 0.736 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".