Patient and Clinician Perspectives: To Create a Better Future for Chronic Kidney Disease, We Need to Talk About Our Kidneys
Bibliographic record
Abstract
Chronic kidney disease (CKD) affects more than one in ten people worldwide. However, results from the REVEAL-CKD study suggest that it is often not diagnosed. Many patients are therefore unaware that they have CKD, putting them at increased risk of disease progression and complications. Empowering patients with knowledge about CKD will allow them to become active participants in their own care, driving improvements in diagnosis rates and changing patient outcomes for the better. In this article, we provide patient and clinician perspectives on the importance of early CKD diagnosis and management. We present an overview of the tests commonly used to diagnose CKD in clinical practice, as well as actionable suggestions for patients, clinicians, and health policymakers that could help improve disease detection and treatment. Navdeep Tangri, a nephrologist and epidemiologist at the University of Manitoba, and Jane DeMeis, a patient living with chronic kidney disease, discuss how results from the REVEAL-CKD study highlight the need for change to improve management of chronic kidney disease. Video Abstract (MP4 141866 KB).
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.048 | 0.016 |
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".