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Neuropsychologic assessment of patients for movement disorder surgery

2000· review· en· W2058338408 on OpenAlexaff
Jean A. Saint‐Cyr, Lisa L. Tr�panier

Bibliographic record

VenueMovement Disorders · 2000
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsYork UniversityCentre for Movement DisordersUniversity of TorontoToronto Western Hospital
Fundersnot available
KeywordsDeep brain stimulationCognitionDementiaPsychologyMovement disordersCognitive disorderPsychiatryMedicineCognitive impairmentDiseaseParkinson's disease

Abstract

fetched live from OpenAlex

The neuropsychologic evaluation of patients under consideration for movement disorder surgery is recognized as being an essential component of the preoperative process. Patients with early-stage concomitant dementia must be identified and the relative risk of postoperative cognitive decline evaluated. Knowledge of the patterns of an individual's strengths and weaknesses might also be a factor in deciding on a neurosurgical procedure. Although the advent of pallidal deep brain stimulation (DBS) has possibly resulted in reduced risk of induced cognitive impairment, even this procedure has been associated with negative sequelae. DBS within the subthalamic nucleus is becoming the method of choice and this may lead to cognitive and behavioral compromise, especially in the elderly patient. The team considering the establishment of neurosurgical treatment is often at a loss to decide how much neuropsychologic testing is required to determine relative risks of cognitive or behavioral morbidity as a consequence of the procedure. A brief summary of expected outcome and of pertinent family process and psychodynamic issues are addressed. This article is intended to serve as a guide to permit clinicians to choose the appropriate length and depth of neuropsychologic assessment, but also to highlight the confounding factors often present in these patients.

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 score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
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 head0.046
GPT teacher head0.359
Teacher spread0.313 · 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

Citations92
Published2000
Admission routes1
Has abstractyes

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