Disparities in the Clinical Provision of Deep Brain Stimulation: A Systematic Scoping Review and Grounded‐Theory Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Deep Brain Stimulation (DBS) has been an established treatment for movement disorders since its FDA approval in 1996. However, disparities in DBS care, particularly concerning race, gender, socioeconomic status, and geography, remain a significant concern globally. OBJECTIVES: This systematic scoping review and grounded theory qualitative analysis aimed to synthesize existing research on worldwide disparities in DBS provision and to develop theoretical solutions to enhance equity and improve the quality of research in DBS disparities. METHODS: A systematic search identified 46 studies, which were critically appraised for quality and analyzed using grounded theory methods to extract core conceptual categories. RESULTS: We characterized three principles of DBS disparities: intersectionality, reciprocal interactivity and influence of patients and providers, and the interposition of simultaneous barriers; together these highlight the role of individual, systemic, and structural factors in generating DBS disparities. Racial minorities, women, socioeconomically disadvantaged individuals, and patients in certain geographic regions were consistently found to have reduced access to DBS. Gaps in the research include a calcified research infrastructure, insufficient attention to cultural and societal contexts, and reliance on conjecture without empirical support. CONCLUSIONS: We propose a multi-level approach to address DBS disparities, including reciprocal education between patients and clinicians, enhanced screening and referral networks, and policy reforms at institutional and governmental levels. These findings will facilitate further hypothesis-driven research and foster more equitable access to DBS globally.
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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 | 0.133 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".