Findings and Implications of the REVEAL-CKD Study Investigating the Global Prevalence of Undiagnosed Stage G3 Chronic Kidney Disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a progressive condition that can lead to kidney failure and the requirement for renal dialysis or transplantation. Early-stage CKD is often missed because the disorder is initially asymptomatic; hence, many patients with CKD already have symptomatic advanced disease (Stages G4–G5) at the time of diagnosis. This is an important issue because the drugs available for the treatment of CKD are most effective when given during the early stages of the disease (Stages G1–G3). EMJ conducted interviews in July 2022 with two key opinion leaders, Navdeep Tangri from the University of Manitoba, Winnipeg, Canada, and Luca De Nicola from the University of Campania Luigi Vanvitell, Naples, Italy, both of whom have a wealth of experience in the management of patients with CKD. The experts provided important insights into the ongoing REVEAL-CKD study, which was designed to explore the global prevalence of undiagnosed Stage G3 CKD. This article describes the main findings of the REVEAL-CKD study published to date and their implications. Possible approaches to improving the diagnosis of CKD are also discussed.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".