Subthalamic Nucleus Stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is currently the most effective surgical treatment for advanced Parkinson disease (PD). Even when the electrode is well positioned in the target, the optimization of clinical results depends on careful programming of electrical parameters and changes in antiparkinsonian drug dosages. OBJECTIVE: To determine whether stable outcomes from subthalamic nucleus DBS for PD can be improved by revising stimulation parameters and drug dosages through "hands-on" involvement of a neurologist expert in both movement disorders and DBS programming. METHODS: In 44 consecutive patients with PD with long-term stable response to subthalamic nucleus DBS (mean +/- SD, 3.5 +/- 1.7 years), we compared scores from the Unified Parkinson's Disease Rating Scale parts II through IV obtained immediately before and following a formal reprogramming of their stimulation. The reprogramming was performed by a neurologist expert in both PD and DBS and accompanied by further medication adjustments. The patients were subsequently followed up for as long as 14 months. RESULTS: In 24 patients (54.6%), the scores on the Unified Parkinson's Disease Rating Scale parts II and III significantly improved by 15.0% and 25.9%, respectively. Anti-PD drugs were significantly reduced (by 25.9%). No improvement was observed in 16 patients (36.4%), and the conditions of 4 patients (9.1%) worsened. CONCLUSIONS: Further improvement of parkinsonian signs can be achieved in the majority of patients even after long-term stable stimulation. Improved patient outcomes from subthalamic nucleus DBS are obtained when postoperative care is personally managed by a neurologist expert in movement disorders and DBS who is directly responsible for stimulation programming and simultaneous drug adjustments based on observed clinical responses to changing stimulation parameters.
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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) | 0.004 | 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".