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Record W1519959924 · doi:10.1002/9781444397970.ch36

Surgery for Non‐Dopaminergic and Non‐Motor Features of Parkinson's Disease

2011· other· en· W1519959924 on OpenAlexaff
Brian Snyder, Andrés M. Lozano

Bibliographic record

VenueParkinson s Disease · 2011
Typeother
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsDeep brain stimulationDopaminergicParkinson's diseaseSubthalamic nucleusLevodopaMedicineNeurosciencePhysical medicine and rehabilitationDiseasePsychologyDopamineInternal medicine

Abstract

fetched live from OpenAlex

Current surgical interventions for Parkinson's disease (PD) are directed at the motor dysfunction of the disease and the motor complications related to levodopa therapy. As pharmacologic and surgical treatments for these specific aspects of PD have improved, it is becoming increasingly clear that the primary reason for disability today relates to the non-motor and non-dopaminergic features of the disease. Less is known about the effects on these features of modulating firing patterns in the globus pallidus pars internus (GPi) and the subthalamic nucleus (STN) with deep brain stimulation (DBS) or ablative lesions. This chapter reviews the effects of GPi and STN-DBS on the non-dopaminergic features of PD, including psychiatric complications, cognitive dysfunction, pain, autonomic dysfunction, and gait disorders. Emerging surgical therapies that attempt to address some of these problems with novel targets are also discussed.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.006

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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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