Enhancing Patient Care by Partnering with Patients in Kidney Health Research
Bibliographic record
Abstract
Background: Canada’s Strategy for Patient-Oriented Research (SPOR) has raised awareness of the need to generate knowledge that is more relevant to patients and to accelerate the translation of evidence into clinical care. Members of the Canadian nephrology community have come together to develop a national patient-oriented research network, Can-SOLVE CKD, that is partnering with patients to close existing gaps in kidney disease knowledge in order to deliver better health outcomes. The Can-SOLVE CKD Network brings together patients and nephrology researchers to transform treatment and care for Canadians living with or at risk for chronic kidney disease. Methods: The network’s 18 research projects are informed by two national prioritysetting exercises conducted with patients, their families and care providers. As the network executes the projects, patients have been integrated into research teams, bringing an enhanced “patient lens” to bear on all aspects of the research life cycle: design, development, recruitment, implementation, and dissemination. Patients are also at the centre of the network’s governance model, which incorporates a Patient Governance Circle and an Indigenous Peoples’ Engagement and Research Council. Results: Can-SOLVE CKD researchers have reported the positive impact of partnering with patients. “My research is better” is often cited as an outcome of patient engagement within the network. Patient partner involvement on the network’s Research Operations Committee has enriched the annual review of projects, resulting in valuable, real-world feedback to project teams. Respondents to the network’s patient engagement survey report feeling better informed about and having greater trust in kidney research as a result of their participation. Conclusions: We have witnessed a shift in the culture of nephrology research in Canada paralleling the broader movement toward patient-oriented research. The traditional role of patients as research subjects has evolved to include patients as valuable and equal members of Can-SOLVE CKD’s research projects. Funding: Government Support - Non-U.S.
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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 | 0.156 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.049 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 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".