Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) is a group of metabolic diseases of multiple etiologies. Although great progress has been made, researchers are still working on the pathogenesis of T2DM and how to best use the treatments available. Aside from several novel pharmacological approaches, catheter-based sympathetic renal denervation (RDN) has gained a significant role in resistant hypertension, as well as improvements in glycemic control in T2DM.In this article, we will summarize herein the role sympathetic activation plays in the progression of T2DM and review the recent clinical RDN experience in glucose metabolism.We performed systematic review in online databases, including PubMed, EmBase, and Web of Science, from inception until 2015.Studies were included if a statistical relationship was investigated between RDN and T2DM.The quality of each included study was assessed by Newcastle-Ottawa scale score. To synthesize these studies, a random-effects model or a fixed-effects model was applied as appropriate. Then, we calculated heterogeneity, performed sensitivity analysis, tested publication bias, and did meta-regression analysis. Finally, we identified 4 eligible articles.In most studies, RDN achieved via novel catheter-based approach using radiofrequency energy has gained a significant role in resistant hypertension, as well as improvements in glycemic control in T2DM. But the DREAMS-Study showed that RDN did not change median insulin sensitivity nor systemic sympathetic activity.Firstly, the current published studies lacked a proper control group, along with the sample capacity was small. Also, data obtained in the subgroups of diabetic patients were not separately analyzed and the follow-up period was very short. In addition, a reduction in blood pressure accounts for the improvements in glucose metabolism and insulin resistance cannot be excluded.If the favorable result of better glucose metabolism is confirmed in large-scale, randomized studies, RDN may emerge as a novel therapeutic option for patients with T2DM.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".