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Record W4415010580 · doi:10.1159/000548814

Disparities in Access to Deep Brain Stimulation

2025· review· en· W4415010580 on OpenAlex
Franziska A. Schmidt, Irene Martínez‐Torres, Jürgen Germann, Mohammad Mehdi Hajiabadi, Oliver Bichsel, Can Sarica, Andrés M. Lozano

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStereotactic and Functional Neurosurgery · 2025
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western HospitalKrembil FoundationUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsDeep brain stimulationBrain stimulationMEDLINEStimulationNeuroimaging

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.345
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it