Patient preferences and priorities for the design of an acute kidney injury prevention trial: Findings from a consensus workshop
Bibliographic record
Abstract
ABSTRACT Introduction High-quality clinical trials are needed to establish the safety, efficacy, and real-world use of potential therapies for acute kidney injury (AKI) prevention. In this consensus workshop, we identified patient and caregiver priorities for recruitment, intervention delivery, and outcomes of a clinical trial of cilastatin to prevent nephrotoxic AKI. Methods We included adults with lived experience of AKI, chronic kidney disease, or risk factors for AKI (e.g., critical care hospitalization), and their caregivers. Using a modified nominal group technique approach, we conducted a series of hybrid in-person/virtual discussions covering 3 clinical trial topic areas: (1) consent and recruitment; (2) intervention delivery; and (3) trial outcomes. Participants voted on their top preferences in each topic area, and discussion transcripts were analyzed inductively using conventional content analysis. Results Thirteen individuals (11 patients, 2 caregivers) participated in the workshop. For consent and recruitment, participants prioritized technology enabled pre-screening and involvement of family members in the consent process. For intervention delivery, participants prioritized measures to facilitate intervention administration and return visits. For trial outcomes, participants identified kidney-related and other clinical outcomes (e.g., AKI, chronic kidney disease, cardiovascular events) as top priorities. Analysis of transcripts provided insight into care team and family involvement in trial-related decisions, implications of allocation to a placebo arm, and impact of participants’ experiences of AKI and critical illness. Conclusion Findings from our workshop will directly inform development of a clinical trial protocol of cilastatin for nephrotoxic AKI prevention and can assist others in patient-centered approaches to AKI trial design.
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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 | 0.228 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".