Challenges in conducting clinical trials in nephrology: conclusions from a Kidney Disease—Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Despite the high costs of treatment of people with kidney disease and associated comorbid conditions, the amount of reliable information available to guide the care of such patients is very limited. Some treatments have been assessed in randomized trials, but most such trials have been too small to detect treatment effects of a magnitude that would be realistic to achieve with a single intervention. Therefore, KDIGO convened an international, multidisciplinary controversies conference titled "Challenges in the Conduct of Clinical Trials in Nephrology" to identify the key barriers to conducting trials in patients with kidney disease. The conference began with plenary talks focusing on the key areas of discussion that included appropriate trial design (covering identification and evaluation of kidney and nonkidney disease outcomes) and sensible trial execution (with particular emphasis on streamlining both design and conduct). Break out group discussions followed in which the key areas of agreement and remaining controversy were identified. Here we summarize the main findings from the conference and set out a range of potential solutions. If followed, these solutions could ensure future trials among people with kidney disease are sufficiently robust to provide reliable answers and are not constrained by inappropriate complexities in design or conduct.
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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 | 0.693 | 0.680 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.039 | 0.033 |
| Open science | 0.013 | 0.023 |
| Research integrity | 0.056 | 0.110 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".