Eligibility Criteria for Deep Brain Stimulation in Parkinson’s Disease, Tremor, and Dystonia
Bibliographic record
Abstract
In this review, the available evidence to guide clinicians regarding eligibility for deep brain stimulation (DBS) in the main conditions in which these forms of therapy are generally indicated-Parkinson's disease (PD), tremor, and dystonia-is presented. In general, the literature shows that DBS is effective for PD, essential tremor, and idiopathic dystonia. In these cases, key points in patient selection must include the level of disability and inability to manage symptoms using the best available medical therapy. Results are, however, still not optimal when dealing with other aetiologies, such as secondary tremors and symptomatic dystonia. Also, in PD, issues such as age and neuropsychiatric profile are still debatable parameters. Overall, currently available literature is able to guide physicians on basic aspects of patient selection and indications for DBS; however, a few points are still debatable and controversial. These issues should be refined and clarified in future studies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".