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Record W2773434309 · doi:10.1002/mds.27228

The changing landscape of surgery for Parkinson's Disease

2017· review· en· W2773434309 on OpenAlexaff
Christopher S. Lozano, Joseph Tam, Andrés M. Lozano

Bibliographic record

VenueMovement Disorders · 2017
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThalamotomyPallidotomyContext (archaeology)Parkinson's diseaseDeep brain stimulationMedicineTransplantationPsychological interventionPsychosurgerySurgeryDiseaseHistoryPsychiatryPathology

Abstract

fetched live from OpenAlex

Neurosurgical interventions have been used to treat PD for over a century. We examined the changing landscape of surgery for PD to appraise the value of various procedures in the context of advances in our understanding and technology. We assessed the number of articles published on neurosurgical procedures for PD over time as an albeit imprecise surrogate for their usage level. We identified over 8,000 publications associated with PD surgery. Over half the publications were on DBS. The field of DBS for PD showed a rapid rise in articles, but is now in a steady state. Thalamotomy and, to a lesser extent, pallidotomy follow a biphasic publication distribution with peaks approximately 30 years apart. Articles on gene therapy and transplantation experienced initial rapid rises and significant recent declines. Procedures using novel technologies, including gamma knife and focused ultrasound, are emerging, but are yet to have significant impact as measured by publication numbers. Pallidotomy and thalamotomy are prominent examples of procedures that were popular, declined, and re-emerged and redeclined. Transplantation and gene therapy have never broken into clinical practice. DBS overtook all procedures as the dominant surgical intervention and drove widespread use of surgery for PD. Notwithstanding, the number of DBS articles appears to have plateaued. As advances continue, emerging treatments may compete with DBS in the future. © 2017 International Parkinson and Movement Disorder Society.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.088
GPT teacher head0.347
Teacher spread0.259 · 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 teacher head, 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

Citations63
Published2017
Admission routes1
Has abstractyes

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