Disparities in Access to Deep Brain Stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is a well-established treatment for several neurological and neuropsychiatric conditions, including movement disorders such as Parkinson's disease, essential tremor, and dystonia, as well as Gilles de la Tourette's syndrome, epilepsy, and obsessive-compulsive disorder. SUMMARY: In recent years, research has expanded to explore the potential of DBS for other indications, including dementia, addiction, disorders of consciousness (e.g., minimally conscious state), and eating disorders. Over the past 3 decades, significant technological advancements have been made in DBS devices, including improvements in electrode design, stimulation parameters, and battery life. However, despite these technological innovations, equitable access to DBS has not progressed at a similar pace. Barriers to access remain a persistent challenge globally, influenced by socioeconomic, geographic, systemic, and policy-related factors. KEY MESSAGE: This review summarizes the current literature on access to DBS, highlighting disparities, challenges, and potential strategies to improve availability and equity in its application.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".