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Record W1977672801 · doi:10.2174/187152711797247795

The Surgical Management of Parkinson's Disease

2011· review· en· W1977672801 on OpenAlexaff
Francisco A. Ponce, Andrés M. Lozano

Bibliographic record

VenueCNS & Neurological Disorders - Drug Targets · 2011
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsDeep brain stimulationLevodopaParkinson's diseaseMedicineDiseaseMovement disordersAdverse effectMotor symptomsDopaminergicSubthalamic nucleusPhysical medicine and rehabilitationIntensive care medicineSurgeryDopamineInternal medicine

Abstract

fetched live from OpenAlex

There has been renewed interest in the surgical treatment of Parkinson's disease (PD) over the past 20 years. In the 1940's to 1960's many PD patients underwent neurosurgical procedures to ablate specific brain targets to alleviate tremor and, to a lesser extent, akinesia and rigidity. With the introduction of levodopa in the 1960s, and the realization of its striking benefits, surgical treatment of movement disorders virtually disappeared. With time, limitations and adverse effects associated with drug treatment became all too apparent. With complications associated with long-term drug treatment, particularly levodopa-induced motor fluctuations and dyskinesias, limiting therapeutic effectiveness in many patients, surgery has been reexamined to address this unmet need. This has lead to the development and, now, widespread adoption of high-frequency deep brain stimulation (DBS). DBS has been shown to be a safe and effective treatment for dopaminergic motor symptoms of PD, particularly tremor, rigidity, and bradykinesia, and has resulted in important reductions in motor complications of medical therapy. While DBS provides important symptomatic benefit, it does not appear to alter the natural history of PD. Other surgical strategies, including cellular transplantation and gene therapy aiming at neural repair and restoration, are currently being examined, but these have yet to be proven useful.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.033
GPT teacher head0.298
Teacher spread0.265 · 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.

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

Citations4
Published2011
Admission routes1
Has abstractyes

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