Knowledge Translation in Glomerulonephritis: Successes in Translational Research From the Bench to Bedside
Bibliographic record
Abstract
Purpose of review: Glomerulonephritis refers to a rare group of diseases characterized by glomerular inflammation, which collectively are a common cause of kidney failure. Until recently, there was a lack of high-quality clinical trials to inform the care of patients with glomerulonephritides. We identified examples of successful translational research spanning from basic science to clinical applications, and highlight gaps in implementation science. Sources of information: The focus of our review was derived from discussions between health care professionals, researchers, and patient partners. We also performed literature searches pertaining to the treatment of glomerulonephritis in PubMed and Google Scholar. Methods: Examples of successful knowledge translation were generated through review of new evidence in the past 5 years and by iterative discussions by the authors. We then conducted a narrative review of several themes related to knowledge translation in glomerulonephritis. This was complemented by an interview with a patient partner to provide an example of a patient's perspective living with glomerulonephritis. Key findings: We summarized selected recent advances in glomerulonephritis and its knowledge translation in the following domains: (1) identification of auto-antibodies in membranous nephropathy and minimal change disease; (2) clinical trials of novel targeted therapies for IgA nephropathy and lupus nephritis, which have led to approval of new treatments; (3) developments in research networks and clinical trials in glomerulonephritis; (4) recognition of the importance in developing standardized patient reported outcome measures in clinical trials; and (5) barriers in knowledge translation including access to medication. Limitations: A systematic search of the literature and formal assessment of quality of evidence were beyond the scope of this review.
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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 | 0.116 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".