Review – current opinion in cardiology hypertension in chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hypertension is a major risk factor for cardiovascular disease, cerebrovascular events, and progression to end-stage kidney disease (ESKD). The kidneys play a causative role in hypertension, but they are also organs vulnerable to hypertensive injury. Thus far, goals for optimal blood pressure in chronic kidney disease (CKD) and ESKD patients are not fully elucidated. Herein, we critically review the existing evidence. RECENT FINDINGS: Large randomized controlled trials (RCTs) continue to be deemed as the best source of evidence to guide optimal blood pressure goals in CKD and ESKD patients. Despite recent advances, the growing body of literature does not permit drawing definitive conclusions. Few adequately powered RCTs have specifically assessed goals for treatment of hypertension in patients with CKD. The most recent large RCT in hypertension, the Systolic Blood Pressure Intervention Trial, included a subset of patients with CKD and provided some insights. For the ESKD population, trials to evaluate blood pressure goals are even more scarce. The Blood Pressure in Dialysis Trial was a relatively small pilot study that can be deemed as hypothesis generating. SUMMARY: Management of hypertension in CKD is essential for optimization of cardiovascular, cerebrovascular and renal outcomes. To date, the existing evidence does not fully clarify ideal targets for blood pressure control in this patient population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".