Characteristics of patients with undiagnosed stage 3 chronic kidney disease: results from an observational study (REVEAL-CKD) in China
Bibliographic record
Abstract
Background Early diagnosis of chronic kidney disease (CKD) is crucial for timely intervention to delay disease progression and improve patient outcomes.However, data for clinical characteristics of Chinese patients with undiagnosed, early-stage CKD are lacking.Methods REVEAL-CKD is a multinational, observational study using real-world data in selected countries to describe factors associated with undiagnosed stage 3 CKD, time to diagnosis, and CKD management post diagnosis.We analysed patient data from 20 hospitals in the China Renal Data System.Adult patients with two consecutive estimated glomerular filtration rate (eGFR) measurements indicating stage 3 CKD (30-<60 ml/min/1.73m 2 ) recorded >90-730 days apart from 2015 to 2020 were eligible.Findings Among 35,222 eligible patients, 25,214 (71.6%) were undiagnosed (lacked a CKD diagnostic code before and up to six months post-second-qualifying-eGFR).Only 2344 (9.3%) undiagnosed patients eventually received a delayed diagnosis, whose median time to diagnosis was 18.1 (95% CI: 17.6-18.8)months.Age ≥65 years, being female, stage 3A CKD, and the absence of nephrology visit and comorbidities (diabetes, established cardiovascular disease, heart failure, hypertension, or chronic nephritic syndrome) were associated with undiagnosed CKD (P < 0.001).Among the diagnosed patients, the proportion receiving ≥1 prescription of guideline-recommended medications (e.g.reninangiotensin system inhibitors) increased and their eGFR decline attenuated post-diagnosis.Interpretation The high proportion of undiagnosed, early-stage CKD, and delayed diagnosis are concerning.The improved prescription patterns and the attenuation of eGFR decline post-diagnosis demonstrate the importance of early diagnosis and timely intervention in CKD patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".