Alteration of heart rate variability in patients with Parkinson's disease after subthalamic nucleus deep brain stimulation: a Meta-analysis
Bibliographic record
Abstract
Objective To evaluate the changes of heart rate variability (HRV) after subthalamic nucleus deep brain stimulation (STN-DBS) in patients with Parkinson's disease (PD). Methods Retrieve relevant cohort studies from online databases (January 1, 2000-December 1, 2017) in PubMed, EMBASE/SCOPUS, Cochrane Online Library, China National Knowledge Infrastructure (CNKI), Wanfang Data and VIP database with key words: subthalamic nucleus, deep brain stimulation, DBS, STN, electrical stimulation, Parkinson disease, heart rate variability. Low-frequency power (LF), high-frequency power (HF) and LF/HF of HRV were applied as evaluation indexes. Quality of studies was evaluated by using Newcastle-Ottawa Scale (NOS). All data were pooled by RevMan 5.3 software for Meta-analysis. Results We enrolled 28 English articles, from which 6 studies with NOS score 7 were chosen after excluding duplicates and those not meeting the inclusion criteria. A total of 101 PD patients undergoing STN-DBS were included. Meta-analysis showed that there were no significant differences in the LF of HRV (SMD = 0.050, 95%CI: -0.230-0.330; P = 0.740), HF of HRV (SMD = 0.160, 95%CI: - 0.120-0.430; P = 0.270), and LF/HF of HRV (SMD = 0.110, 95%CI: -0.220-0.440; P = 0.500) in patients with PD before and after the treatment of STN-DBS. Conclusions STN-DBS does not change HRV of patients with PD.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".