Development of a Clinical Research Agenda for Acute Kidney Injury Using an International, Interdisciplinary, Three-Step Modified Delphi Process
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although acute kidney injury is common and significantly increases the risk for intensive care unit and hospital mortality, little is known about its true incidence or how it can be prevented. Furthermore, key unanswered questions remain about the optimal diagnosis and treatment of patients with acute kidney injury. An international, consensus-based, prioritized research agenda was sought to guide clinical and translational research in acute kidney injury. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A three-step modified Delphi process involving 43 participants representing 19 professional societies, organizations, and multiple stakeholder groups ranging from clinical practice to basic science research was conducted. RESULTS: Twenty research questions were generated across six focus groups. Overall, research priorities generated from nephrologists and intensivists were similar and highly correlated. The stakeholder groups included members from 15 countries. Results from adult and pediatric groups showed important differences, as did results from developing compared with developed countries; however the priority rankings from the developed and developing countries were significantly correlated. Top research priorities in acute kidney injury include determining optimal timing of renal replacement therapy and improving the understanding of the epidemiology of acute kidney injury around the world. CONCLUSIONS: Research recommendations that are highly consistent across various stakeholder groups and between developed and developing countries have been produced. It is hoped that these recommendations will prove valuable in guiding future clinical and translational research in this area.
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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 | 0.351 | 0.232 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".