SUN-215 PATIENT ENGAGEMENT IN KNOWLEDGE TRANSLATION: A COLLABORATIVE MODEL FOR MOVING KIDNEY HEALTH RESEARCH INTO PRACTICE
Bibliographic record
Abstract
Effective knowledge translation is the process of moving research evidence into clinical practice. Can-SOLVE CKD is a pan-Canadian patient-oriented kidney research network with an established Knowledge User (KU) and Translation (KT) Committee that includes two patient partners as integral members. This committee provides guidance, expertise, and direction for all KT activities undertaken by research projects within the network and ensures KT approaches are patient centered. In this presentation, we will define key concepts related to KT, outline the role of the KU/KT Committee in supporting kidney health research, and highlight the contributions of patient partners on this committee.
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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 | 0.152 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".