The Future of Clinical Trials in Chronic Renal Disease: Outcome of an NIH/FDA/Physician Specialist Conference
Bibliographic record
Abstract
For people with chronic renal insufficiency, the therapeutic goal is to prevent progression to end-stage renal disease, a serious condition that can only be treated with dialysis and kidney transplantation. Although restriction of dietary protein slows the progression of renal disease somewhat, the principal treatment to slow chronic renal disease is appropriate reduction of blood pressure. Antihypertensive agents, particularly those that produce sustained, long-term reductions in proteinuria, such as angiotensin-converting enzyme inhibitors, not only decrease blood pressure but also preserve renal function. Clinical trials to evaluate these and other drug therapies in renal disease progression have used both "hard end points" (e.g., dialysis, transplantation, death) and intermediate end points of renal disease progression (e.g., doubling of serum creatinine concentration, reductions in proteinuria). Trials that have used hard end points typically recruited patients with advanced renal disease to demonstrate a difference in therapies within a period of 2 to 5 years. However, proteinuria reduction, along with a decrease in the time to doubling of serum creatinine in very early diabetic renal disease, could demonstrate an altered natural history of renal disease. Although hard end points are indicators of a drug's efficacy in reducing cardiovascular events or preserving renal function, they do not assess the impact of a treatment on altering the natural history of early renal disease. For clinical trials of people with all but the most advanced renal disease, use of intermediate end points of renal disease progression is the only practical option for assessment of treatment efficacy and effectiveness. Given the available data on proteinuria reduction and doubling of serum creatinine from clinical trials, these end points, taken together, appear to provide an acceptable means of assessing a treatment's impact on slowing renal disease progression.
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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 | 0.316 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.032 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".