Setting Research Priorities for Patients on or Nearing
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 perspectiveof adultpatientsonornearingdialysis,theircaregivers,andthe healthcareprofessionalswhocarefor 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. Clin J Am Soc Nephrol 9: 1813–1821, 2014. doi: 10.2215/CJN.01610214
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 | 0.358 | 0.414 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.032 | 0.030 |
| Open science | 0.008 | 0.048 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".