Prescription of renin‐angiotensin‐aldosterone system inhibitors (RAASi) and its determinants in patients with advanced CKD under nephrologist care
Bibliographic record
Abstract
Renin-angiotensin-aldosterone system inhibitors (RAASi) are recommended for chronic kidney disease (CKD) patients. In this study, we describe RAASi prescription patterns in the Chronic Kidney Disease Outcomes and Practice Patterns Study (CKDopps) in Brazil, Germany, France, and the United States (US). 5870 patients (mean age 66-72 years; congestive heart failure [CHF] in 11%-19%; diabetes in 43%-54%; serum potassium ≥5 in 20%-35%) were included. RAASi prescription was more common in Germany (80%) and France (77%) than Brazil (66%) and the United States (52%), where the prevalence of prescription decreases particularly in patients with CKD stage 5. In the multivariable regression model, RAASi prescription was least common in the United States and more common in patients who were younger, had diabetes, hypertension, or less advanced CKD. In conclusion, RAASi prescription patterns vary by country, and by demographic and clinical characteristics. RAASi appear to be underused, even among patients with strong class-specific recommendations. Although the reasons for this variation could not be fully identified in this cross-sectional observation, our data indicate that the risk of hyperkalemia may contribute to the underuse of this class of agents in moderate to advanced CKD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".