Long-term Efficacy of Subthalamic Nucleus Deep Brain Stimulation in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Subthalamic nucleus deep brain stimulation (STN DBS) is effective against advanced Parkinson's disease (PD), allowing dramatic improvement of Parkinsonism, in addition to a significant reduction in medication. Here we aimed to investigate the long-term effect of STN DBS in Chinese PD patients, which has not been thoroughly studied in China. METHODS: Ten PD patients were assessed before DBS and followed up 1, 3, and 5 years later using Unified Parkinson's Disease Rating Scale Part III (UPDRS III), Parkinson's Disease Questionnatire-39, Parkinson's Disease Sleep Scale-Chinese Version, Mini-mental State Examination, Montreal Cognitive Assessment, Hamilton Anxiety Scale and Hamilton Depression Scale. Stimulation parameters and drug dosages were recorded at each follow-up. Data were analyzed using the ANOVA for repeated measures. RESULTS: In the "off" state (off medication), DBS improved UPDRS III scores by 35.87% in 5 years, compared with preoperative baseline (P < 0.001). In the "on" state (on medication), motor scores at 5 years were similar to the results of preoperative levodopa challenge test. The quality of life is improved by 58.18% (P < 0.001) from baseline to 3 years and gradually declined afterward. Sleep, cognition, and emotion were mostly unchanged. Levodopa equivalent daily dose was reduced from 660.4 ± 210.1 mg at baseline to 310.6 ± 158.4 mg at 5 years (by 52.96%, P < 0.001). The average pulse width, frequency and amplitude at 5 years were 75.0 ± 18.21 μs, 138.5 ± 19.34 Hz, and 2.68 ± 0.43 V, respectively. CONCLUSIONS: STN DBS is an effective intervention for PD, although associated with a slightly diminished efficacy after 5 years. Compared with other studies, patients in our study required lower voltage and medication for satisfactory symptom control.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".