Changing paradigms of studies in kidney diseases
Bibliographic record
Abstract
Recognizing kidney disease as a major global health issue, the International Society of Nephrology convened a 2-day international, multi-stakeholder meeting to develop a road map for advancing clinical research in nephrology. The meeting focused on promoting the use of patient-reported outcome measures, moving beyond single biomarker targets, adopting innovative trial designs, and incorporating hierarchical composite end points. Participants included clinicians, trialists, regulators, patient partners, and industry experts invited from all International Society of Nephrology regions. Discussions emphasized the importance of inclusive trial design, validation of patient-reported outcome measures, predictive enrichment strategies, and broader trial accessibility across resource settings. Key recommendations included enhancing diversity in trial populations, avoiding overreliance on isolated biomarkers, adopting novel study designs, strengthening public-private partnerships, and validating composite end points. A coordinated effort was deemed essential to implement these strategies in both research and practice, ensuring sustainable progress and reducing the global burden of kidney disease.
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 | 0.631 | 0.492 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.019 | 0.008 |
| Science and technology studies | 0.010 | 0.151 |
| Scholarly communication | 0.040 | 0.062 |
| Open science | 0.016 | 0.032 |
| Research integrity | 0.019 | 0.073 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".