8. Efficacy of Renal Denervation on Office Blood Pressure in Resistant Hypertension: a Systematic Review
Bibliographic record
Abstract
Hypertension almost occurs in about a quarter of the world's population and is still increasing, causing a burden in public health. Renal denervation is one of the strategies to treat resistant hypertension. However, studies on the efficacy of renal denervation in patients with resistant hypertension are still limited and not well known. The purpose of this study was to find out the efficacy of renal denervation in patients with resistant hypertension by office blood pressure measurement. This review was conducted on 10–17 December 2022. Two independent researchers systematically extracted data from several databases, such as PubMed Central (PMC), Science Direct, and PUBMED by using MeSH terminology of keywords renal denervation, hypertension, and safety. The extracted studies were then analyzed and selected according to our inclusion criteria such as studies in the last 5 years, cohort studies, and case-control studies. We excluded systematic reviews, meta-analyses, case series, case reports, studies on pregnant women, children, and animals. Research quality was assessed using Newcastle-Ottawa (NOS). From 5 cohort studies (1605 subjects from various countries), all of them showed renal denervation resulted in better and sustained office blood pressure control on long-term follow up; All studies have proven good quality based on NOS. In conclusion, renal denervation showed efficacy by sustained blood pressure reducing effect in patients with resistant hypertension. However, further study is needed to confirm these findings.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".