Translational Research in Kidney Transplantation and the Role of Patient Engagement
Bibliographic record
Abstract
BACKGROUND: Translational research is an evolving discipline that is intended to bridge the gaps between basic science research, clinical research, and implementation in clinical practice. It is a fluid, multidirectional process that requires strong interdisciplinary collaboration to produce research that is relevant to end-users. PURPOSE OF THIS REVIEW: This review summarizes current perspectives on translational research and outlines its relevance and importance to kidney transplantation research. SOURCES OF INFORMATION: Sources of information used for this review include published reports, articles, and research funding websites. FINDINGS: Tissue typing is used as an in-depth example of how translational research has been applied in the field of kidney transplant medicine, and how it has resulted in successful implementation of diagnostic and management options for sensitized individuals undergoing kidney transplantation. The value of actively involving kidney transplant stakeholders (patients, caregivers, and clinicians) in setting research priorities and determining relevant outcomes for future investigation is also discussed. LIMITATIONS: This is a narrative review of the literature which has been partly influenced by the perspectives and experiences of its authors. IMPLICATIONS: Translational and patient-oriented research practices should be incorporated into future research endeavours in the field of kidney transplantation in order to create beneficial change in clinical practice and improve patient outcomes. WHAT WAS KNOWN BEFORE: Translational research which engages patients in the investigative process can enhance the likelihood that medical discoveries will have a meaningful impact at the bedside. WHAT THIS ADDS: This article applies current perspectives on translational research and patient engagement to the field of kidney transplantation, illustrating how these approaches have led to significant advancements in the field. It provides further justification for deliberate, targeted efforts to cross-collaborate and incorporate the patient voice into kidney transplant research.
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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 | 0.199 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 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".