Patient-Reported Outcomes in Patients with Chronic Kidney Disease and Kidney Transplant—Part 1
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a complex medical condition that is associated with several comorbidities and requires comprehensive medical management. Given the chronic nature of the condition, its frequent association with psychosocial distress, and its very significant symptom burden, the subjective patient experience is key toward understanding the true impact of CKD on the patients' life. Patient-reported outcome measures are important tools that can be used to support patient-centered care and patient engagement during the complex management of patients with CKD. The routine collection and use of patient-reported outcomes (PROs) in clinical practice may improve quality of care and outcomes, and may provide useful data to understand the disease from both an individual and a population perspective. Many tools used to measure PROs focus on assessing health-related quality of life, which is significantly impaired among patients with CKD. Health-related quality of life, in addition to being an important outcome itself, is associated with clinical outcomes such as health care use and mortality. In Part 1 of this review, we provide an overview of PROs and implications of their use in the context of CKD. In Part 2, we will review the selection of appropriate measures and the relevant domains of interest for patients with CKD.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".