International consensus definitions of clinical trial outcomes for kidney failure: 2020
Bibliographic record
Abstract
Kidney failure is an important outcome for patients, clinicians, researchers, healthcare systems, payers, and regulators. However, no harmonized international consensus definitions of kidney failure and key surrogates of progression to kidney failure exist specifically for clinical trials. The International Society of Nephrology convened an international multi-stakeholder meeting to develop consensus on this topic. A core group, experienced in design, conduct, and outcome adjudication of clinical trials, developed a database of 64 randomized trials and the 163 included definitions relevant to kidney failure. Using an iterative process, a set of proposed consensus definitions were developed and subsequently vetted by the larger multi-stakeholder group of 83 participants representing 18 different countries. The consensus of the meeting participants was that clinical trial kidney failure outcomes should be comprised of a composite that includes receipt of a kidney transplant, initiation of maintenance dialysis, and death from kidney failure; it may also include outcomes based solely on laboratory measurements of glomerular filtration rate: a sustained low glomerular filtration rate and a sustained percent decline in glomerular filtration rate. Discussion included important considerations, such as (i) recognition of existing nomenclature for kidney failure; (ii) applicability across resource settings; (iii) ease of understanding for all stakeholders; and (iv) avoidance of inappropriate complexity so that the definitions can be used across ranges of populations and trial methodologies. The final definitions reflect the consensus for use in clinical trials.
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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 | 0.304 | 0.375 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".