Determining the research priorities for patients with chronic kidney disease not on dialysis
Bibliographic record
Abstract
BACKGROUND: The importance of engaging key stakeholders, and patients in particular, in determining research priorities has been recognized. We sought to identify the top 10 research priorities for patients with non-dialysis chronic kidney disease (CKD), their caregivers, and the clinicians and policy-makers involved in their care. METHODS: We used the four-step James Lind Alliance process to establish the top 10 research priorities. A national survey of patients with non-dialysis CKD (estimated glomerular filtration rate <45 mL/min/1.73 m 2 ), their caregivers, and the clinicians and policy-makers involved in their care was conducted to identify research uncertainties. A Steering Group of patients, caregivers, clinicians and researchers combined and reduced these uncertainties to 30 through a series of iterations. Finally, a workshop with participants from across Canada (12 patients, 6 caregivers, 3 physicians, 2 nurses, 1 pharmacist and 1 policy-maker) was held to determine the top 10 research priorities, using a nominal group technique. RESULTS: Overall, 439 individuals responded to the survey and identified 1811 uncertainties, from which the steering group determined the top 30 uncertainties to be considered at the workshop. The top 10 research uncertainties prioritized at the workshop included questions about treatments to prevent progression of kidney disease (including diet) and to treat symptoms of CKD, provider- and patient-targeted strategies for managing CKD, the impact of lifestyle on disease progression, harmful effects of medications on disease progression, optimal strategies for treatment of cardiovascular disease in CKD and for early identification of kidney disease, and strategies for equitable access to care for patients with CKD. CONCLUSIONS: We identified the top 10 research priorities for patients with CKD that can be used to guide researchers, as well as inform funders of health-care research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.239 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".