Setting Research Priorities for Patients on or Nearing Dialysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With increasing emphasis among health care providers and funders on patient-centered care, it follows that patients and their caregivers should be included when priorities for research are being established. This study sought to identify the most important unanswered questions about the management of kidney failure from the perspective of adult patients on or nearing dialysis, their caregivers, and the health care professionals who care for these patients. Research uncertainties were identified through a national Canadian survey of adult patients on or nearing dialysis, their caregivers, and health care professionals. Uncertainties were refined by a steering committee that included patients, caregivers, researchers, and clinicians to assemble a short-list of the top 30 uncertainties. Thirty-four people (11 patients; five caregivers; eight physicians; six nurses; and one social worker, pharmacist, physiotherapist, and dietitian each) from across Canada subsequently participated in a workshop to determine the top 10 research questions. In total, 1570 usable research uncertainties were received from 317 respondents to the survey. Among these, 259 unique uncertainties were identified; after ranking, these were reduced to a short-list of 30 uncertainties. During the in-person workshop, the top 10 research uncertainties were identified, which included questions about enhanced communication among patients and providers, dialysis modality options, itching, access to kidney transplantation, heart health, dietary restrictions, depression, and vascular access. These can be used alongside the results of other research priority-setting exercises to guide researchers in designing future studies and inform health care funders.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it