Therapeutic Considerations in Preventing Chronic Kidney Disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) affects 35.5 million US adults, but most patients are unaware of their diagnosis. Screening for CKD at-risk individuals is required, as symptoms do not appear until advanced stages. The combination of urine albumin-to-creatinine ratio and estimated glomerular filtration rate permits the classification of CKD stages and the determination of risk of CKD progression and cardiovascular disease, which is the most common cause of death in CKD. Cardiovascular-kidney-metabolic syndrome highlights the complex interplay between the heart, kidney, and metabolic disorders, such as diabetes and dysfunctional obesity, which promotes chronic inflammation, leading to injury in these organs and systems. New guideline-directed medical therapies consisting of sodium-glucose cotransporter 2 inhibitors, glucose-like peptide-1 receptor agonists, and nonsteroidal mineralocorticoid receptor antagonists, in addition to standard-of-care therapies including angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, have revolutionized CKD management, which may be best facilitated through a multidisciplinary care approach.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".