Prevalence and progression of chronic kidney disease among patients with type <scp>2</scp> diabetes: Insights from the <scp>DISCOVER</scp> study
Bibliographic record
Abstract
Abstract We report the prevalence and change in severity of chronic kidney disease (CKD) in DISCOVER, a global, 3‐year, prospective, observational study of patients with type 2 diabetes (T2D) initiating second‐line glucose‐lowering therapy. CKD stages were defined according to estimated glomerular filtration rate (eGFR). Overall, 7843 patients from 35 countries had a baseline serum creatinine measurement. Of these (56.7% male; mean age: 58.1 years; mean eGFR: 87.5 mL/min/1.73 m 2 ), baseline prevalence estimates for stage 0‐1, 2, 3 and 4‐5 CKD were 51.4%, 37.7%, 9.4% and 1.4%, respectively. A total of 5819 patients (74.2%) also had at least one follow‐up serum creatinine measurement (median time between measurements: 2.9 years, interquartile range: 1.9‐3.0 years). Mean eGFR decreased slightly to 85.7 mL/min/1.73 m 2 over follow‐up. CKD progression (increase of ≥1 stage) occurred in 15.7% of patients, and regression (decrease of ≥1 stage) in 12.0%. In summary, a substantial proportion of patients with T2D developed CKD or had CKD progression after the initiation of second‐line therapy. Renal function should be regularly monitored in these patients, to ensure early CKD diagnosis and treatment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".