Summary of Research: Dapagliflozin Utilization in Chronic Kidney Disease and Its Real-World Effectiveness Among Patients with Lower Levels of Albuminuria in the USA and Japan
Bibliographic record
Abstract
This is a summary of the original article 'Dapagliflozin Utilization in Chronic Kidney Disease and Its Real-World Effectiveness Among Patients with Lower Levels of Albuminuria in the USA and Japan'. The slowing down of kidney function decline is important for managing chronic kidney disease (CKD) and preventing its complications. Clinical trials of dapagliflozin, a sodium-glucose cotransporter-2 inhibitor (SGLT-2i), have shown reductions in disease progression and death in patients with CKD and elevated levels of albuminuria. This summary of research provides an overview of a previously published article that aimed to find out whether dapagliflozin is also effective in patients with lower levels of albuminuria [urinary albumin-to-creatinine ratio (UACR) below 200 mg/g]. Starting dapagliflozin was associated with slower kidney function decline in patients with CKD and UACR below 200 mg/g compared with not starting. This effect was also observed in a subgroup analysis of patients without type 2 diabetes. These results suggest that the established benefits of SGLT-2is may extend to patients with lower levels of albuminuria.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".