Knowledge translation for nephrologists: strategies for improving the identification of patients with proteinuria
Bibliographic record
Abstract
For health scientists, knowledge translation refers to the process of facilitating uptake of knowledge into clinical practice or decision making. Since high-quality clinical research that is not applied cannot improve outcomes, knowledge translation is critical for realizing the value and potential for all types of health research. Knowledge translation is particularly relevant for areas within health care where gaps in care are known to exist, which is the case for some areas of management for people with chronic kidney disease (CKD), including assessment of proteinuria. Given that proteinuria is a key marker of cardiovascular and renal risk, forthcoming international practice guidelines will recommend including proteinuria within staging systems for CKD. While this revised staging system will facilitate identification of patients at higher risk for progression of CKD and mortality who benefit from intervention, strategies to ensure its appropriate uptake will be particularly important. This article describes key elements of effective knowledge translation strategies based on the knowledge-to-action cycle framework and describes options for effective knowledge translation interventions related to the new CKD guidelines, focusing on recommendations related to assessment for proteinuria specifically. The article also presents findings from a multidisciplinary meeting aimed at developing knowledge translation intervention strategies, with input from key stakeholders (researchers, knowledge users, decision makers and collaborators), to facilitate implementation of this guideline. These considerations are relevant for dissemination and implementation of guidelines on other topics and in other clinical settings.
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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 | 0.055 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".