Opportunities for Engaging Patients in Kidney Research
Bibliographic record
Abstract
PURPOSE: The purpose of this review is to provide a summary of the rationale for engaging patients in research as well as to review the established and envisioned advantages and strategies for patient-researcher partnerships. The authors of this article, which include a patient and 4 researchers in kidney disease, discuss the expected benefits and opportunities for patient engagement in their respective research programs. The 4 research programs span the spectrum of kidney disease and focus on enhancing bone health, increasing living donor kidney transplants, improving medication adherence, and preventing kidney transplant rejection. SOURCES OF INFORMATION: The sources of information for this review include published studies on the topics of patient engagement and the 4 research programs of the new investigators. KEY FINDINGS: (1) Patient, health care provider, and researcher partnerships can contribute useful insights capable of enhancing research in kidney disease. (2) Regardless of the research program, there are various strategies and opportunities for engagement of patients with lived experience across the various stages of research in kidney disease. (3) Envisioned advantages of patient-researcher partnerships include: targeting patient-identified research priorities, integrating patients' experiential knowledge, improving study design and feasibility through patient-researcher input, facilitating dissemination of research findings to other patients, effectively responding to patient concerns about studies, and inspiring researchers to conduct their research. LIMITATIONS: The limitations of the current review include the relative scarcity of literature on patient engagement within the field of kidney disease. IMPLICATIONS: The findings of the current review suggest that it will be important for future studies to identify optimal strategies for patient engagement in setting research priorities, study design, participant recruitment, execution of research projects, and knowledge dissemination and translation.
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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 | 0.142 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".