A scoping review of neuromodulation techniques for controlling blood pressure: what are the ups and downs to this approach?
Bibliographic record
Abstract
Abstract Background and Objectives While there has been rapid progress in research aimed at developing device-based neuromodulation therapies for blood pressure (BP) disorders, there is a paucity of FDA-approved therapies. Currently, the only approved devices for treating resistant hypertension use renal denervation, however, this could soon change as clinical research progresses. With the evolution of interventional strategies for BP regulation, it is important to comprehend the developments to date in order to gauge directions for future research. The objective of this scoping review was to provide the current range of device-based BP neuromodulation approaches, overview salient characteristics of the included studies, address limitations, and detail avenues of further investigation. Methods Our review was conducted using the Preferred Reporting Items for Reviews and and Meta-analysis framework. The literature search was performed across the Web of Science Core Collection, Scopus, and Pubmed databases. The search yielded 3503 studies, of which 100 studies remained following the screening process. In the last 10 years, there has been an increase in the number of experimental neurostimulation studies detailing increases and decreases in BP. Of all the included studies, most adopted a non-randomized experimental approach (89%), used animal participants (65%), used invasive neuromodulation methods (74%), and performed acute experiments (84%). More studies documented only depressor responses (49%) compared to pressor responses (35%), and 13% reported both pressor and depressor responses using multiple neural targets. Conclusions This review addressed developments in device-based BP neuromodulation, highlighting a dominant focus on treating resistant hypertension compared to hypotensive disorders. While advancements in implantable electrodes have improved spatial selectivity of stimulation, non-invasive neurostimulation devices have become increasingly popular in recent years.
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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 | 0.031 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".