Effects of intensive blood pressure control on mortality and cardiorenal function in chronic kidney disease patients
Bibliographic record
Abstract
Blood pressure (BP) variability is highly correlated with cardiovascular and kidney outcomes in patients with chronic kidney disease (CKD). However, appropriate BP targets in patients with CKD remain uncertain. We searched PubMed, Embase, and the Cochrane Library for randomized controlled trials (RCTs) of CKD patients who underwent intensive BP management. Kappa score was used to assess inter-rater agreement. A good agreement between the authors was observed to inter-rater reliability of RCTs selection (kappa = 0.77; P = 0.005). Ten relevant studies involving 20 059 patients were included in the meta-analysis. Overall, intensive BP management may reduce the incidence of cardiovascular disease mortality (RR: 0.69, 95% CI: 0.53 to 0.90, P: 0.01), all-cause mortality (RR: 0.77, 95% CI: 0.67 to 0.88, P < 0.01) and composite cardiovascular events (RR: 0.84 95% CI: 0.75 to 0.95, P < 0.01) in patients with CKD. However, reducing BP has no significant effect on the incidence of doubling of serum creatinine level or 50% reduction in GFR (RR: 1.26, 95% CI: 0.66 to 2.40, P = 0.48), composite renal events (RR 1.07, 95% CI: 0.81 to 1.41, P = 0.64) or SAEs (RR: 0.97, 95% CI: 0.90 to 1.05, P = 0.48). In patients with CKD, enhanced BP management is associated with reduced all-cause mortality, cardiovascular mortality, and incidence of composite cardiovascular events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".