Supramaximal Dose of Candesartan in Proteinuric Renal Disease
Bibliographic record
Abstract
High levels of proteinuria predict renal deterioration, suggesting that interventions to reduce proteinuria may postpone the development of severe renal impairment. This multicenter Canadian trial evaluated whether supramaximal dosages of candesartan would reduce proteinuria to a greater extent than the maximum approved antihypertensive dosage. The authors randomly assigned 269 patients who had persistent proteinuria (> or =1 g/d) despite 7 wk of treatment with the highest approved dosage of candesartan (16 mg/d) to 16, 64, or 128 mg/d candesartan for 30 wk. The median serum creatinine level was 130.0 micromol/L (1.47 mg/dl), and the median urinary protein excretion was 2.66 g/d; most (53.9%) patients had diabetic nephropathy. The mean difference of the percentage change in proteinuria for patients receiving 128 mg/d candesartan compared with those receiving 16 mg/d candesartan was -33.05% (95% confidence interval -45.70 to -17.44; P < 0.0001). Reductions in BP were not different across the three treatment groups. Elevated serum potassium levels (K+ > 5.5 mEq/L) led to the early withdrawal of 11 patients, but there were no dosage-related increases in adverse events. In conclusion, proteinuria that persists despite treatment with the maximum recommended dosage of candesartan can be reduced by increasing the dosage of candesartan further, but serum potassium levels should be monitored during 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".