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Record W2015996179 · doi:10.1159/000070103

The Effects of Posteroventral Pallidotomy on Balance Function in Patients with Parkinson’s Disease

2002· article· en· W2015996179 on OpenAlexaff
Brian D. Westerberg, Joseph B. Roberson, Brad A. Stach, Gerald D. Silverberg, Gary Heit

Bibliographic record

VenueStereotactic and Functional Neurosurgery · 2002
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPallidotomyParkinson's diseaseMedicineBalance (ability)Physical medicine and rehabilitationDiseaseNeurosciencePsychologyDeep brain stimulationInternal medicine

Abstract

fetched live from OpenAlex

Parkinson's disease is a chronic, progressive neurodegenerative disorder resulting from dopaminergic cell loss in the pars compacta of the substantia nigra. Conventional treatment of Parkinson's disease consists of pharmacological replacement of dopamine. A treatment alternative, posteroventral pallidotomy (PVP), has been used for medically intractable stages of the disease. The purpose of this study was to evaluate the effects of PVP on balance function, as measured by dynamic posturography, in patients with medically intractable Parkinson's disease. Five subjects were studied within 2 days prior to and within 6 months following PVP. Pretreatment abnormalities were found in vestibular, visual, and somatosensory processing in balance function. Posteroventral pallidotomy resulted in improvement in vestibular compensation of posture in some patients, which may be at least partially due to an improvement in latencies to respond to changes in stance. Dynamic posturography is an effective tool in the evaluation of balance and posture in patients with advanced Parkinson's disease.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.191
Teacher spread0.183 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations15
Published2002
Admission routes1
Has abstractyes

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