Unmet needs in clinical trials in CKD: questions we have not answered and answers we have not questioned
Bibliographic record
Abstract
Many advances have been made in the field of nephrology over the last decade. These include an increasing focus on patient-centred involvement in trials, exploration of innovative trial designs and methodology, the growth of personalized medicine and, most importantly, novel therapeutic agents that are disease-modifying for large groups of patients with and without diabetes and chronic kidney disease. Despite this progress, many questions remain unanswered and we have not critically evaluated some of our assumptions, practices and guidelines despite emerging evidence to challenge current paradigms and discrepant patient-preferred outcomes. How best to implement best practices, diagnose various conditions, examine better diagnostic tools, treat laboratory values versus patients and understand prediction equations in the clinical context remain unanswered. As we enter a new era in nephrology, there are extraordinary opportunities to change the culture and care. Rigorous research paradigms enabling both the generation and the use of new information should be explored. We identify here some key areas of interest and suggest renewed efforts to describe and address these gaps so that we can develop, design and execute trials of importance to all.
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 | 0.180 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.029 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".