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Record W4210573052 · doi:10.1055/s-0041-1742266

Neuropsychiatric Treatments for Parkinson's Disease: Nonpharmacological Approaches

2022· review· en· W4210573052 on OpenAlexaff
Neha Mathur, Haseel Bhatt, Sarah C. Lidstone

Bibliographic record

VenueSeminars in Neurology · 2022
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversity of TorontoToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsMedicineMovement disordersParkinson's diseaseDiseaseAnxietyPhysical medicine and rehabilitationMotor symptomsDepression (economics)RehabilitationMultidisciplinary approachCognitionQuality of life (healthcare)PsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

Although diagnosed by characteristic motor features, Parkinson's disease and other movement disorders are frequently accompanied by a wide range of neuropsychiatric symptoms that require a multidisciplinary approach for treatment. Neuropsychiatric symptoms such as depression, anxiety and cognitive symptoms strongly influence quality of life, motor symptoms, and non-motor bodily symptoms. This review summarizes our current understanding of the neuropsychiatric symptoms in movement disorders and discusses the evidence base for treatments focusing on rehabilitation and nonpharmacological approaches. A practical approach is then proposed for patient selection for specific treatments based on disease stage. The article focuses mostly on Parkinson's disease as a prototypical movement disorder with the largest evidence base but the principles discussed herein are applicable to a range of other movement disorders.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.104
GPT teacher head0.350
Teacher spread0.246 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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