Chronic Kidney Disease Stage G4 in a Diabetic Patient Improved by Multi-Disciplinary Treatments Based Upon Literature Search for Therapeutic Evidence
Bibliographic record
Abstract
In the EMPA-REG OUTCOME trial, sodium-glucose cotransporter 2 (SGLT2) inhibitor, empagliflozin, reduced incident or worsening nephropathy. In the LEADER trial, a glucagon-like peptide 1 (GLP-1) receptor agonist, liraglutide, resulted in lower rates of the development and progression of diabetic kidney disease than placebo. Therefore, the American Diabetes Association and the European Association for the Study of Diabetes recommend the decision to treat high-risk individuals with a GLP-1 receptor agonist or SGLT2 inhibitor to reduce chronic kidney disease (CKD) progression should be considered. A 72-year-old male obese diabetic patient developed CKD stage G4 despite of use of both SGLT2 inhibitor and GLP-1 receptor agonist. We started using sodium bicarbonate because he showed metabolic acidosis due to uremia. We also started to use spherical carbonaceous adsorbent which adsorbs indole, the precursor of indoxyl sulfate, uremic toxin. We started the treatment with finerenone, a nonsteroidal, selective mineralocorticoid receptor antagonist, which has been recently shown to lower risks of CKD progression. Considering unfavorable effects of hyperuricemia on CKD, to treat his hyperuricemia, we started to use dotinurad, a novel selective urate reabsorption inhibitor, which reduces serum urate levels by selective inhibition of urate transporter 1. The improvement of CKD stage G4 in a diabetic patient was obtained by such multi-disciplinary treatments in addition to SGLT2 inhibitor and GLP-1 receptor agonist.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".