4. Safety of Renal Denervation as Treatment for Resistant Hypertension: A Systematic Review
Bibliographic record
Abstract
Hypertension is very common throughout the world's population, which many people are unaware of, but it is still rising in population and causing a major public health problem. One of the methods used in treating resistant hypertension is renal denervation. However, studies on its safety and vascular complications effects are still unknown. The purpose of this study was to find out the safety of renal denervation in patients with resistant hypertension. 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 4 cohort studies (166 subjects from various countries), 3 of them showed renal denervation has a favorable safety profile in gaining blood pressure control. The safety endpoint such as all-cause death and vascular complication were reached, but one study reported one patient with progression of renal artery stenosis and hypertensive urgency; 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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".