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Record W2124487735 · doi:10.1002/mds.25911

Subthalamic stimulation improves motor function but not home and neighborhood mobility

2014· article· en· W2124487735 on OpenAlexafffund
Jean‐François Daneault, Christian Duval, Sébastien Barbat‐Artigas, Mylène Aubertin‐Leheudre, Nicolas Jodoin, Michel Panisset, Abbas F. Sadikot

Bibliographic record

VenueMovement Disorders · 2014
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill UniversityUniversité du Québec à MontréalInstitut Universitaire de Gériatrie de MontréalMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health Research
KeywordsDeep brain stimulationQuality of life (healthcare)Physical medicine and rehabilitationParkinson's diseaseSubthalamic nucleusMotor functionMedicinePsychologyRating scalePhysical therapyDiseaseInternal medicineDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Subthalamic (STN) deep brain stimulation (DBS) is a recognized therapy for alleviating motor symptoms of Parkinson's disease (PD). However, little is known about its impact on mobility, an important component of quality of life (QoL). To address this issue, we assessed the impact of STN DBS on life-space mobility and QoL. METHODS: Twenty surgical patients with PD were assessed using mobility and QoL scales and the United Parkinson's disease rating scale, and results were compared before surgery and 6 to 9 months postoperatively. RESULTS: STN DBS significantly improved motor dysfunction but had a limited impact on measures of life-space mobility and QoL. INTERPRETATION: STN DBS improves motor function and some components of QoL. However, motor recovery does not translate into improved life-space in the intermediate term. In addition to a focus on motor function, multidisciplinary attention to increasing mobility may further improve QoL in the intermediate and long-term.

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 score0.114
Threshold uncertainty score0.597

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 head0.009
GPT teacher head0.225
Teacher spread0.217 · 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

Citations9
Published2014
Admission routes2
Has abstractyes

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