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Record W1993329601 · doi:10.1002/mds.20151

Subthalamic nucleus deep brain stimulation for parkinson's disease after successful pallidotomy: Clinical and electrophysiological observations

2004· article· en· W1993329601 on OpenAlexaff
Galit Kleiner‐Fisman, David N. Fisman, Orit Zamir, Jonathan O. Dostrovsky, Elspeth Sime, Jean A. Saint‐Cyr, Andrés M. Lozano, Anthony E. Lang

Bibliographic record

VenueMovement Disorders · 2004
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western HospitalMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsPallidotomyDeep brain stimulationSubthalamic nucleusDyskinesiaParkinson's diseaseParkinsonismMovement disordersMedicineCentral nervous system diseasePhysical medicine and rehabilitationPsychologyLevodopaNeurological disorderRating scaleAnesthesiaNeuroscienceDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Unilateral pallidotomy is an effective treatment for contralateral parkinsonism and dyskinesia, yet symptoms progress in many patients. Little is known about whether such patients obtain a useful response to subsequent bilateral subthalamic nucleus deep brain stimulation (STN DBS). Changes in Unified Parkinson's Disease Rating Scale (UPDRS) Motor and Activities of Daily Living (ADL) scores, medication requirements, and dyskinesias were measured. Clinical outcomes were compared to patients with de novo STN DBS. Neuronal recordings were performed. STN DBS resulted in a significant reduction in UPDRS Motor scores (42.1%; 95% confidence interval [CI], 26.9-57.4; P = 0.03), comparable with de novo STN DBS surgery (41%; 95% CI, 26-46%; P < 0.001). There was also less change in dyskinesia duration and disability scores (P = 0.017, 0.005). There were no side-to-side differences clinically or in the STN neuronal firing rates and patterns. Bilateral STN DBS is safe and efficacious in improving motor symptoms in patients with prior pallidotomy.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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 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

Citations47
Published2004
Admission routes1
Has abstractyes

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