Rationale and Clinical Pearls for Primary Care Doctors Referring Patients for Deep Brain Stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is a surgical treatment involving the implantation of a brain lead connected to a chest-based neurostimulator similar to a cardiac pacemaker. The device can be programmed to deliver electrical impulses to neuromodulate abnormal brain circuitry in disorders such as Parkinson's disease (PD), essential tremor (ET), and dystonia. As the number of patients receiving DBS surgery increases, it will be important for primary care doctors to identify reasonable DBS candidates for referral to an experienced center. OBJECTIVE: To provide primary care physicians with a rationale and also to provide clinically useful pearls for referral of potential DBS candidates. METHODS: A complete PubMed review of the literature. RESULTS: This review will be focused on PD and ET and will address the following issues: what are the common motor and nonmotor symptoms? What is the evidence supporting the use of DBS in PD and ET? What is the importance of a multi- or interdisciplinary DBS team for patient selection? What can be done to improve success in identifying and referring potential DBS candidates? CONCLUSION: DBS is a highly effective therapy for select candidates with PD and ET. The most important factor influencing DBS outcome is proper patient selection. It will be critical as DBS continues to be more commonly employed for primary care doctors to select candidates from their practices as appropriate referrals to specialized centers.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".